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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68333
Volume:
10 |
10 |
Oct 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Information Science and Engineering
Jawaharlal Nehru New College of Engineering
Information Science and Engineering
Jawaharlal Nehru New College of Engineering
Information Science and Engineering
Jawaharlal Nehru New College of Engineering
Information Science and Engineering
Jawaharlal Nehru New College of Engineering
Information Science and Engineering
Jawaharlal Nehru New College of Engineering
Navigating personal health information online frequently feels fragmented and overwhelming. Users often find themselves switching between separate tools to check symptoms, estimate dietary needs, understand PDF lab reports, or look up reliable wellness advice. To solve this issue, we built StrawHealth a unified digital health assistant using Flask, SQLAlchemy, and SQLite. The platform integrates five core tools into one easy-to-use web interface: (1) structured symptom screening powered by machine learning, (2) chronic diabetes risk calculation, (3) rule-based dietary planning, (4) medical report parsing using Optical Character Recognition (OCR), and (5) a Retrieval-Augmented Generation (RAG) assistant for general health questions. We thoroughly evaluated our machine learning pipelines using synthetic patient data and public health survey records. To ensure realistic testing and avoid memorization, the symptom classification pipeline was evaluated on 12,000 synthetic records using group-aware cross-validation. Logistic Regression performed best among evaluated algorithms, achieving a mean cross-validated macro F1-score of 0.7887 ± 0.0092 and a test accuracy of 78.69%. For chronic diabetes risk screening, a Histogram Gradient Boosting model trained on 253,680 CDC survey records achieved an ROC-AUC of 0.8294 and a high sensitivity (recall) of 81.31% on 38,052 unseen test records after threshold tuning (T = 0.1306). We also detail our PyMuPDF/Tesseract document parsing pipeline and hybrid RAG system. StrawHealth is presented as an open academic decision-support prototype demonstrating how integrated, privacy-first software design can make digital health guidance more accessible while keeping strict clinical boundaries and disclaimers.
KEYWORDS
StrawHealth demonstrates a unified, practical approach to personal digital health assistance. By integrating symptom screening, chronic diabetes risk calculation, automated lab report parsing, rule-based nutrition planning, and retrieval-augmented health Q&A into a single responsive Flask web application, it addresses the fragmentation of existing online tools. Empirical evaluation establishes robust performance across core machine learning components, achieving a 78.83% macro F1-score on synthetic symptom data and an 82.94% ROC-AUC on public health-risk data. StrawHealth serves as a transparent, open-source prototype demonstrating how modular software integration and responsible ethical disclaimers can make digital health decision-support tools safer and more accessible.
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Publication Date
10/26/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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