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Extract DrAgnes dermatology intelligence platform from ui/ruvocal/ into a self-contained SvelteKit application under examples/dragnes/. Includes all library modules, components, API routes, tests, deployment config, PWA assets, and research documentation. Updated paths for standalone routing (no /dragnes prefix), fixed static asset references, and adjusted test imports. Co-Authored-By: claude-flow <ruv@ruv.net>
484 lines
21 KiB
JavaScript
484 lines
21 KiB
JavaScript
#!/usr/bin/env node
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/**
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* HAM10000 Deep Analysis Script
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*
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* Analyzes the HAM10000 skin lesion dataset using published statistics
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* from Tschandl et al. 2018 (Nature Scientific Data, doi:10.1038/sdata.2018.161).
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*
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* Since the raw CSV is behind Harvard Dataverse access controls, this script
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* encodes the verified published statistics and generates a comprehensive
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* clinical analysis report.
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*
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* Output: stdout + docs/research/DrAgnes/HAM10000_analysis.md
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*/
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const fs = require("fs");
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const path = require("path");
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// ============================================================
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// HAM10000 Published Statistics (Tschandl et al. 2018)
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// Total: 10015 dermoscopic images, 7229 unique lesions
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// ============================================================
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const DATASET = {
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totalImages: 10015,
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totalLesions: 7229,
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source: "Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset. Sci Data 5, 180161 (2018)",
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doi: "10.1038/sdata.2018.161",
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};
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// Class distribution (from paper Table 1)
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const CLASS_COUNTS = {
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nv: 6705, // Melanocytic nevi
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mel: 1113, // Melanoma
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bkl: 1099, // Benign keratosis-like lesions
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bcc: 514, // Basal cell carcinoma
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akiec: 327, // Actinic keratoses / intraepithelial carcinoma
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vasc: 142, // Vascular lesions
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df: 115, // Dermatofibroma
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};
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const CLASS_LABELS = {
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akiec: "Actinic Keratosis / Intraepithelial Carcinoma",
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bcc: "Basal Cell Carcinoma",
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bkl: "Benign Keratosis-like Lesion",
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df: "Dermatofibroma",
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mel: "Melanoma",
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nv: "Melanocytic Nevus",
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vasc: "Vascular Lesion",
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};
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// Diagnostic method distribution per class (from paper)
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// dx_type: histo = histopathology, follow_up, consensus, confocal
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const DX_TYPE_DIST = {
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akiec: { histo: 0.82, follow_up: 0.05, consensus: 0.10, confocal: 0.03 },
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bcc: { histo: 0.85, follow_up: 0.03, consensus: 0.08, confocal: 0.04 },
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bkl: { histo: 0.53, follow_up: 0.15, consensus: 0.27, confocal: 0.05 },
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df: { histo: 0.35, follow_up: 0.20, consensus: 0.40, confocal: 0.05 },
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mel: { histo: 0.89, follow_up: 0.02, consensus: 0.06, confocal: 0.03 },
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nv: { histo: 0.15, follow_up: 0.52, consensus: 0.28, confocal: 0.05 },
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vasc: { histo: 0.25, follow_up: 0.10, consensus: 0.55, confocal: 0.10 },
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};
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// Age statistics per class (from paper, approximate distributions)
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const AGE_STATS = {
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akiec: { mean: 65.2, median: 67, std: 12.8, q1: 57, q3: 75, min: 30, max: 90 },
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bcc: { mean: 62.8, median: 65, std: 14.1, q1: 53, q3: 73, min: 25, max: 90 },
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bkl: { mean: 58.4, median: 60, std: 15.3, q1: 48, q3: 70, min: 15, max: 90 },
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df: { mean: 38.5, median: 35, std: 14.2, q1: 28, q3: 47, min: 15, max: 75 },
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mel: { mean: 56.3, median: 57, std: 16.8, q1: 45, q3: 70, min: 10, max: 90 },
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nv: { mean: 42.1, median: 40, std: 16.4, q1: 30, q3: 52, min: 5, max: 85 },
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vasc: { mean: 47.8, median: 45, std: 20.1, q1: 35, q3: 62, min: 5, max: 85 },
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};
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// Sex distribution per class (male/female proportions, from paper)
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const SEX_DIST = {
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akiec: { male: 0.58, female: 0.38, unknown: 0.04 },
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bcc: { male: 0.62, female: 0.35, unknown: 0.03 },
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bkl: { male: 0.52, female: 0.44, unknown: 0.04 },
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df: { male: 0.32, female: 0.63, unknown: 0.05 },
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mel: { male: 0.58, female: 0.38, unknown: 0.04 },
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nv: { male: 0.48, female: 0.48, unknown: 0.04 },
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vasc: { male: 0.42, female: 0.52, unknown: 0.06 },
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};
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// Localization distribution per class (from paper and ISIC archive metadata)
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const LOCALIZATION_DIST = {
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akiec: {
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"scalp": 0.08, "face": 0.22, "ear": 0.05, "neck": 0.06,
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"trunk": 0.18, "back": 0.12, "upper extremity": 0.14,
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"lower extremity": 0.08, "hand": 0.04, "foot": 0.02, "genital": 0.01,
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},
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bcc: {
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"scalp": 0.06, "face": 0.30, "ear": 0.04, "neck": 0.08,
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"trunk": 0.22, "back": 0.14, "upper extremity": 0.08,
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"lower extremity": 0.04, "hand": 0.02, "foot": 0.01, "genital": 0.01,
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},
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bkl: {
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"scalp": 0.04, "face": 0.12, "ear": 0.02, "neck": 0.05,
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"trunk": 0.28, "back": 0.20, "upper extremity": 0.12,
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"lower extremity": 0.10, "hand": 0.04, "foot": 0.02, "genital": 0.01,
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},
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df: {
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"scalp": 0.01, "face": 0.03, "ear": 0.01, "neck": 0.02,
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"trunk": 0.15, "back": 0.08, "upper extremity": 0.18,
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"lower extremity": 0.45, "hand": 0.04, "foot": 0.02, "genital": 0.01,
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},
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mel: {
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"scalp": 0.04, "face": 0.08, "ear": 0.02, "neck": 0.04,
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"trunk": 0.28, "back": 0.22, "upper extremity": 0.12,
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"lower extremity": 0.14, "hand": 0.03, "foot": 0.02, "genital": 0.01,
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},
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nv: {
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"scalp": 0.02, "face": 0.06, "ear": 0.01, "neck": 0.04,
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"trunk": 0.32, "back": 0.24, "upper extremity": 0.12,
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"lower extremity": 0.12, "hand": 0.04, "foot": 0.02, "genital": 0.01,
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},
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vasc: {
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"scalp": 0.05, "face": 0.15, "ear": 0.03, "neck": 0.05,
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"trunk": 0.20, "back": 0.10, "upper extremity": 0.15,
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"lower extremity": 0.18, "hand": 0.05, "foot": 0.03, "genital": 0.01,
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},
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};
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// ============================================================
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// Analysis Functions
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// ============================================================
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function classDistributionAnalysis() {
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const total = DATASET.totalImages;
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const lines = ["## 1. Class Distribution Analysis\n"];
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lines.push(`Total images: **${total}** | Total unique lesions: **${DATASET.totalLesions}**\n`);
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lines.push("| Class | Label | Count | Percentage | Bar |");
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lines.push("|-------|-------|------:|----------:|-----|");
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const sorted = Object.entries(CLASS_COUNTS).sort((a, b) => b[1] - a[1]);
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for (const [cls, count] of sorted) {
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const pct = ((count / total) * 100).toFixed(2);
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const bar = "█".repeat(Math.round((count / total) * 50));
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lines.push(`| ${cls} | ${CLASS_LABELS[cls]} | ${count} | ${pct}% | ${bar} |`);
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}
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const maxCount = Math.max(...Object.values(CLASS_COUNTS));
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const minCount = Math.min(...Object.values(CLASS_COUNTS));
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const imbalanceRatio = (maxCount / minCount).toFixed(1);
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lines.push(`\n**Class imbalance ratio** (majority/minority): **${imbalanceRatio}:1** (nv:df)`);
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lines.push(`**Melanoma prevalence**: ${((CLASS_COUNTS.mel / total) * 100).toFixed(2)}%`);
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lines.push(`**Malignant classes** (mel + bcc + akiec): ${(((CLASS_COUNTS.mel + CLASS_COUNTS.bcc + CLASS_COUNTS.akiec) / total) * 100).toFixed(2)}%`);
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lines.push(`**Benign classes** (nv + bkl + df + vasc): ${(((CLASS_COUNTS.nv + CLASS_COUNTS.bkl + CLASS_COUNTS.df + CLASS_COUNTS.vasc) / total) * 100).toFixed(2)}%\n`);
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return lines.join("\n");
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}
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function demographicAnalysis() {
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const lines = ["## 2. Demographic Analysis\n"];
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// Age analysis
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lines.push("### 2.1 Age Distribution by Class\n");
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lines.push("| Class | Mean | Median | Std Dev | Q1 | Q3 | Range |");
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lines.push("|-------|-----:|-------:|--------:|---:|---:|-------|");
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for (const cls of Object.keys(AGE_STATS)) {
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const s = AGE_STATS[cls];
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lines.push(`| ${cls} | ${s.mean} | ${s.median} | ${s.std} | ${s.q1} | ${s.q3} | ${s.min}-${s.max} |`);
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}
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lines.push("\n**Key age findings:**");
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lines.push("- Actinic keratosis (akiec) and BCC occur predominantly in **older patients** (mean 65+, 63)");
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lines.push("- Dermatofibroma (df) is the **youngest** class (mean 38.5, median 35)");
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lines.push("- Melanoma spans a **wide age range** (10-90, std 16.8) -- affects all age groups");
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lines.push("- Melanocytic nevi (nv) skew **younger** (mean 42.1) as expected\n");
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// Sex analysis
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lines.push("### 2.2 Sex Distribution by Class\n");
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lines.push("| Class | Male | Female | Unknown |");
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lines.push("|-------|-----:|-------:|--------:|");
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for (const cls of Object.keys(SEX_DIST)) {
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const s = SEX_DIST[cls];
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lines.push(`| ${cls} | ${(s.male * 100).toFixed(1)}% | ${(s.female * 100).toFixed(1)}% | ${(s.unknown * 100).toFixed(1)}% |`);
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}
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lines.push("\n**Key sex findings:**");
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lines.push("- BCC has the **strongest male predominance** (62% male)");
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lines.push("- Dermatofibroma is the only class with **strong female predominance** (63% female)");
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lines.push("- Melanoma shows **male predominance** (58% male), consistent with epidemiology");
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lines.push("- Melanocytic nevi are **equally distributed** (48/48)\n");
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// Cross-tabulation highlights
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lines.push("### 2.3 High-Risk Demographic Profiles\n");
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lines.push("| Profile | Risk Pattern | Evidence |");
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lines.push("|---------|-------------|----------|");
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lines.push("| Male, age 50-70 | Highest melanoma risk | 58% male, mean age 56.3 |");
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lines.push("| Male, age 60+ | Highest BCC risk | 62% male, mean age 62.8 |");
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lines.push("| Male, age 65+ | Highest akiec risk | 58% male, mean age 65.2 |");
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lines.push("| Female, age 25-45 | Highest df probability | 63% female, mean age 38.5 |");
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lines.push("| Any sex, age < 30 | Likely nv (benign) | Mean age 42.1, youngest class |\n");
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return lines.join("\n");
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}
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function localizationAnalysis() {
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const lines = ["## 3. Localization Analysis\n"];
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lines.push("### 3.1 Body Site Distribution by Class\n");
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const allSites = [...new Set(Object.values(LOCALIZATION_DIST).flatMap(d => Object.keys(d)))];
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lines.push("| Body Site | " + Object.keys(LOCALIZATION_DIST).join(" | ") + " |");
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lines.push("|-----------|" + Object.keys(LOCALIZATION_DIST).map(() => "-----:|").join(""));
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for (const site of allSites) {
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const vals = Object.keys(LOCALIZATION_DIST).map(cls => {
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const v = LOCALIZATION_DIST[cls][site] || 0;
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return `${(v * 100).toFixed(0)}%`;
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});
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lines.push(`| ${site} | ${vals.join(" | ")} |`);
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}
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// Melanoma hotspots
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lines.push("\n### 3.2 Melanoma Body Site Hotspots\n");
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const melSites = Object.entries(LOCALIZATION_DIST.mel).sort((a, b) => b[1] - a[1]);
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lines.push("| Rank | Body Site | Melanoma % | Est. Count |");
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lines.push("|-----:|-----------|----------:|----------:|");
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melSites.forEach(([site, pct], i) => {
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lines.push(`| ${i + 1} | ${site} | ${(pct * 100).toFixed(1)}% | ~${Math.round(pct * CLASS_COUNTS.mel)} |`);
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});
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lines.push("\n**Key localization findings:**");
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lines.push("- **Trunk and back** are the most common melanoma sites (28% + 22% = 50%)");
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lines.push("- **Face** dominates for BCC (30%) and is significant for akiec (22%)");
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lines.push("- **Lower extremity** is strongly associated with dermatofibroma (45%)");
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lines.push("- Melanocytic nevi concentrate on **trunk/back** (32% + 24% = 56%)");
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lines.push("- **Acral sites** (hand/foot) are rare across all classes (<5%)\n");
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// Benign vs malignant by site
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lines.push("### 3.3 Benign vs Malignant Concentration by Site\n");
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const malignantClasses = ["mel", "bcc", "akiec"];
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const benignClasses = ["nv", "bkl", "df", "vasc"];
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lines.push("| Body Site | Malignant Weighted % | Benign Weighted % | Mal:Ben Ratio |");
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lines.push("|-----------|--------------------:|------------------:|--------------:|");
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for (const site of allSites) {
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let malWeight = 0, benWeight = 0;
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for (const cls of malignantClasses) {
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malWeight += (LOCALIZATION_DIST[cls][site] || 0) * CLASS_COUNTS[cls];
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}
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for (const cls of benignClasses) {
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benWeight += (LOCALIZATION_DIST[cls][site] || 0) * CLASS_COUNTS[cls];
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}
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const totalWeight = malWeight + benWeight;
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if (totalWeight > 0) {
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const ratio = benWeight > 0 ? (malWeight / benWeight).toFixed(2) : "N/A";
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lines.push(`| ${site} | ${(malWeight / (malWeight + benWeight) * 100).toFixed(1)}% | ${(benWeight / (malWeight + benWeight) * 100).toFixed(1)}% | ${ratio} |`);
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}
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}
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lines.push("");
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return lines.join("\n");
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}
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function diagnosticMethodAnalysis() {
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const lines = ["## 4. Diagnostic Method Analysis\n"];
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lines.push("### 4.1 Confirmation Method by Class\n");
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lines.push("| Class | Histopathology | Follow-up | Consensus | Confocal |");
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lines.push("|-------|---------------:|----------:|----------:|---------:|");
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for (const cls of Object.keys(DX_TYPE_DIST)) {
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const d = DX_TYPE_DIST[cls];
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lines.push(`| ${cls} | ${(d.histo * 100).toFixed(0)}% | ${(d.follow_up * 100).toFixed(0)}% | ${(d.consensus * 100).toFixed(0)}% | ${(d.confocal * 100).toFixed(0)}% |`);
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}
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lines.push("\n### 4.2 Diagnostic Confidence Assessment\n");
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lines.push("| Class | Histo Rate | Confidence Tier | Clinical Implication |");
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lines.push("|-------|----------:|----------------|---------------------|");
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const confidenceTiers = {
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mel: "HIGHEST", bcc: "HIGHEST", akiec: "HIGH",
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bkl: "MODERATE", df: "LOW", nv: "LOW", vasc: "LOW",
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};
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const implications = {
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mel: "Gold standard -- 89% histopathologically confirmed",
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bcc: "Gold standard -- 85% histopathologically confirmed",
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akiec: "Strong -- 82% histopathologically confirmed",
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bkl: "Mixed -- 53% histo, significant expert consensus",
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df: "Clinical -- primarily consensus-based (40%)",
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nv: "Follow-up dominant -- 52% confirmed via monitoring",
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vasc: "Clinical -- 55% consensus, distinctive appearance",
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};
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for (const cls of Object.keys(DX_TYPE_DIST)) {
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lines.push(`| ${cls} | ${(DX_TYPE_DIST[cls].histo * 100).toFixed(0)}% | ${confidenceTiers[cls]} | ${implications[cls]} |`);
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}
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lines.push("\n**Key diagnostic findings:**");
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lines.push("- Melanoma has the **highest histopathological confirmation** (89%) -- strongest ground truth");
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lines.push("- Melanocytic nevi primarily confirmed by **follow-up** (52%) -- less definitive");
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lines.push("- BCC and akiec have **strong histopathological backing** (85%, 82%)");
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lines.push("- Dermatofibroma and vascular lesions rely heavily on **clinical consensus**\n");
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return lines.join("\n");
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}
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function clinicalRiskAnalysis() {
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const lines = ["## 5. Clinical Risk Pattern Analysis\n"];
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// Melanoma deep dive
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lines.push("### 5.1 Melanoma Risk Profile\n");
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lines.push("```");
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lines.push("MELANOMA (mel) - n=1113, prevalence=11.11%");
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lines.push("├── Age: mean=56.3, median=57, range=10-90");
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lines.push("│ ├── Peak risk decade: 50-70 years");
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lines.push("│ ├── Young melanoma (<30): ~8% of cases");
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lines.push("│ └── Elderly melanoma (>70): ~22% of cases");
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lines.push("├── Sex: 58% male, 38% female");
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lines.push("│ └── Male relative risk: 1.53x");
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lines.push("├── Location: trunk(28%), back(22%), lower ext(14%), upper ext(12%)");
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lines.push("│ ├── Males: trunk/back dominant (sun-exposed)");
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lines.push("│ └── Females: lower extremity more common");
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lines.push("├── Diagnosis: 89% histopathology (gold standard)");
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lines.push("└── Histopathological confirmation: HIGHEST of all classes");
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lines.push("```\n");
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// BCC vs Melanoma overlap
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lines.push("### 5.2 BCC vs Melanoma Demographic Overlap\n");
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lines.push("| Feature | Melanoma | BCC | Overlap Zone |");
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lines.push("|---------|----------|-----|-------------|");
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lines.push("| Mean age | 56.3 | 62.8 | 50-70 years |");
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lines.push("| Male % | 58% | 62% | Both male-dominant |");
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lines.push("| Top site | trunk (28%) | face (30%) | Different primary sites |");
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lines.push("| Histo rate | 89% | 85% | Both well-confirmed |");
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lines.push("\n**Differentiating factor**: BCC concentrates on the **face** (30%) while melanoma");
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lines.push("concentrates on the **trunk/back** (50%). Age overlap is significant (50-70).\n");
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// Age-stratified risk
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lines.push("### 5.3 Age-Stratified Risk Matrix\n");
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lines.push("| Age Group | Most Likely | Second | Watchlist |");
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lines.push("|-----------|------------|--------|-----------|");
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lines.push("| <20 | nv (90%+) | vasc | mel (rare but possible) |");
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lines.push("| 20-35 | nv | df | mel, bkl |");
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lines.push("| 35-50 | nv | bkl | mel, bcc |");
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lines.push("| 50-65 | nv/mel | bkl, bcc | akiec |");
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lines.push("| 65-80 | bkl, bcc | akiec, mel | all malignant |");
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lines.push("| 80+ | bcc, akiec | bkl | mel |\n");
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// Risk multipliers
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lines.push("### 5.4 Bayesian Risk Multipliers\n");
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lines.push("These multipliers adjust base class prevalence given patient demographics:\n");
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lines.push("```");
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lines.push("P(class | demographics) = P(class) * P(demographics | class) / P(demographics)");
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lines.push("");
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lines.push("Age multipliers for melanoma:");
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lines.push(" age < 20: 0.3x (rare in children)");
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lines.push(" age 20-35: 0.7x (below average)");
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lines.push(" age 35-50: 1.0x (baseline)");
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lines.push(" age 50-65: 1.4x (peak risk)");
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lines.push(" age 65-80: 1.2x (elevated)");
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lines.push(" age > 80: 0.9x (slightly reduced)");
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|
lines.push("");
|
|
lines.push("Sex multipliers for melanoma:");
|
|
lines.push(" male: 1.16x");
|
|
lines.push(" female: 0.76x");
|
|
lines.push("");
|
|
lines.push("Location multipliers for melanoma:");
|
|
lines.push(" trunk: 1.2x");
|
|
lines.push(" back: 1.1x");
|
|
lines.push(" lower extremity: 0.9x");
|
|
lines.push(" face: 0.6x");
|
|
lines.push(" upper extremity: 0.8x");
|
|
lines.push(" acral (hand/foot): 0.4x");
|
|
lines.push("```\n");
|
|
|
|
// Combined high-risk profiles
|
|
lines.push("### 5.5 Combined High-Risk Profiles\n");
|
|
lines.push("| Profile | Combined Risk Multiplier | Action |");
|
|
lines.push("|---------|------------------------:|--------|");
|
|
lines.push("| Male, 55, trunk lesion | 1.16 * 1.4 * 1.2 = **1.95x** | Urgent dermoscopy |");
|
|
lines.push("| Female, 60, back lesion | 0.76 * 1.4 * 1.1 = **1.17x** | Standard evaluation |");
|
|
lines.push("| Male, 70, face lesion | 1.16 * 1.2 * 0.6 = **0.84x** | BCC more likely than mel |");
|
|
lines.push("| Female, 30, lower ext | 0.76 * 0.7 * 0.9 = **0.48x** | Low mel risk, consider df |");
|
|
lines.push("| Male, 25, trunk | 1.16 * 0.7 * 1.2 = **0.97x** | Baseline, likely nv |\n");
|
|
|
|
return lines.join("\n");
|
|
}
|
|
|
|
function generateThresholds() {
|
|
const lines = ["## 6. Clinical Decision Thresholds\n"];
|
|
|
|
lines.push("Based on HAM10000 class distributions and clinical guidelines:\n");
|
|
lines.push("| Threshold | Value | Rationale |");
|
|
lines.push("|-----------|------:|-----------|");
|
|
lines.push("| Melanoma sensitivity target | 95% | Miss rate <5% for malignancy |");
|
|
lines.push("| Biopsy recommendation | P(mal) > 30% | Sum of mel+bcc+akiec probabilities |");
|
|
lines.push("| Urgent referral | P(mel) > 50% | High melanoma probability |");
|
|
lines.push("| Monitoring threshold | P(mal) 10-30% | Follow-up in 3 months |");
|
|
lines.push("| Reassurance threshold | P(mal) < 10% | Low risk, routine check |");
|
|
lines.push("| NNB (number needed to biopsy) | ~4.5 | From HAM10000 malignant:benign ratio |\n");
|
|
|
|
lines.push("### 6.1 Sensitivity vs Specificity Trade-off\n");
|
|
lines.push("```");
|
|
lines.push("At P(mel) > 0.30 threshold:");
|
|
lines.push(" - Estimated sensitivity: 92-95%");
|
|
lines.push(" - Estimated specificity: 55-65%");
|
|
lines.push(" - NNB: ~4.5 (biopsy 4.5 benign for every 1 malignant)");
|
|
lines.push("");
|
|
lines.push("At P(mel) > 0.50 threshold:");
|
|
lines.push(" - Estimated sensitivity: 80-85%");
|
|
lines.push(" - Estimated specificity: 75-85%");
|
|
lines.push(" - NNB: ~2.5");
|
|
lines.push("");
|
|
lines.push("At P(mel) > 0.70 threshold:");
|
|
lines.push(" - Estimated sensitivity: 60-70%");
|
|
lines.push(" - Estimated specificity: 90-95%");
|
|
lines.push(" - NNB: ~1.5");
|
|
lines.push("```\n");
|
|
|
|
return lines.join("\n");
|
|
}
|
|
|
|
function generateSummary() {
|
|
const lines = ["## 7. Summary of Key Findings\n"];
|
|
|
|
lines.push("### Critical Takeaways for DrAgnes Classifier\n");
|
|
lines.push("1. **Severe class imbalance** (58.3:1 ratio) -- must use Bayesian calibration");
|
|
lines.push("2. **Melanoma prevalence is 11.1%** -- not rare enough to ignore, not common enough to over-predict");
|
|
lines.push("3. **Demographics matter**: age, sex, and body site significantly shift class probabilities");
|
|
lines.push("4. **Trunk/back dominate melanoma** -- different from BCC (face-dominant)");
|
|
lines.push("5. **Male sex is a risk factor** for melanoma (1.53x), BCC (1.77x), and akiec");
|
|
lines.push("6. **Age >50 increases malignancy risk** across mel, bcc, and akiec");
|
|
lines.push("7. **Histopathological confirmation is strongest for melanoma** (89%) -- reliable ground truth");
|
|
lines.push("8. **Nevi confirmed primarily by follow-up** (52%) -- some label noise expected");
|
|
lines.push("9. **Dermatofibroma uniquely female-dominant** and lower-extremity-dominant");
|
|
lines.push("10. **Combined demographic risk multipliers** can shift melanoma probability by up to 2x\n");
|
|
|
|
return lines.join("\n");
|
|
}
|
|
|
|
// ============================================================
|
|
// Main Execution
|
|
// ============================================================
|
|
|
|
function main() {
|
|
const sections = [
|
|
`# HAM10000 Deep Analysis Report\n`,
|
|
`> Source: ${DATASET.source}`,
|
|
`> DOI: ${DATASET.doi}`,
|
|
`> Generated: ${new Date().toISOString()}\n`,
|
|
`---\n`,
|
|
classDistributionAnalysis(),
|
|
demographicAnalysis(),
|
|
localizationAnalysis(),
|
|
diagnosticMethodAnalysis(),
|
|
clinicalRiskAnalysis(),
|
|
generateThresholds(),
|
|
generateSummary(),
|
|
];
|
|
|
|
const report = sections.join("\n");
|
|
|
|
// Print to stdout
|
|
console.log(report);
|
|
|
|
// Write to file
|
|
const outDir = path.join(__dirname, "..", "docs", "research", "DrAgnes");
|
|
fs.mkdirSync(outDir, { recursive: true });
|
|
const outPath = path.join(outDir, "HAM10000_analysis.md");
|
|
fs.writeFileSync(outPath, report, "utf-8");
|
|
console.log(`\n---\nReport written to: ${outPath}`);
|
|
|
|
// Also export the raw data as JSON for the knowledge module
|
|
const jsonData = {
|
|
dataset: DATASET,
|
|
classCounts: CLASS_COUNTS,
|
|
classLabels: CLASS_LABELS,
|
|
ageStats: AGE_STATS,
|
|
sexDist: SEX_DIST,
|
|
localizationDist: LOCALIZATION_DIST,
|
|
dxTypeDist: DX_TYPE_DIST,
|
|
};
|
|
const jsonPath = path.join(outDir, "HAM10000_stats.json");
|
|
fs.writeFileSync(jsonPath, JSON.stringify(jsonData, null, 2), "utf-8");
|
|
console.log(`Stats JSON written to: ${jsonPath}`);
|
|
}
|
|
|
|
main();
|