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Paper ID:IJSREM68333

StrawHealth: A Modular AI-Assisted Healthcare Information Platform with Symptom Screening, Health-Risk Assessment, Medical Report Parsing, Nutrition Rules, and Retrieval-Augmented Guidance

Stream/Area:

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Subject:

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Paper ID:

IJSREM68333

Volume:

10 |

10 |

Oct 2026

Pages:

1-8

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

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.

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Authors

RS

Rakesh S

Information Science and Engineering

Jawaharlal Nehru New College of Engineering

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SR

Saqlain Raza

Information Science and Engineering

Jawaharlal Nehru New College of Engineering

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DMP

Darshan M. Patil

Information Science and Engineering

Jawaharlal Nehru New College of Engineering

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PD

Partha Dinda

Information Science and Engineering

Jawaharlal Nehru New College of Engineering

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Mentors

RR

Rashmi R

Information Science and Engineering

Jawaharlal Nehru New College of Engineering

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Abstract

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

Healthcare information platforms symptom screening synthetic datasets Logistic Regression health-risk assessment Histogram Gradient Boosting OCR medical report parsing Retrieval-Augmented Generation Flask framework.

Cite This Article

Rakesh S, Saqlain Raza, Darshan M. Patil, Partha Dinda, Rashmi R, "StrawHealth: A Modular AI-Assisted Healthcare Information Platform with Symptom Screening, Health-Risk Assessment, Medical Report Parsing, Nutrition Rules, and Retrieval-Augmented Guidance," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-8, Oct 2026.

Full Article/Conclusion

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.

References

[1] H. L. Semigran, J. A. Linder, C. Gidengil, and A. Mehrotra, "Evaluation of symptom checkers for self diagnosis and triage: audit study," BMJ, vol. 351, p. h3480, 2015, doi: 10.1136/bmj.h3480. [2] F. Rustam, Z. Imtiaz, A. Mehmood, V. Rupapara, G. S. Choi, S. Din, and I. Ashraf, "Automated disease diagnosis and precaution recommender system using supervised machine learning," Multimedia Tools and Applications, vol. 81, no. 22, pp. 31929–31952, 2022, doi: 10.1007/s11042-022-12897-2. [3] R. H. Koushal, C. V. S. Pranav, R. K. Gupta, K. H. Verma, and N. C. Mair, "AI-Powered Symptom Checker Chatbot in Regional Languages," in Proc. 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2025, doi: 10.1109/IATMSI64286.2025.10985720. [4] S. Gaikwad, P. Awatade, Y. Sirdeshmukh, and C. Prasad, "Diet Plan and Home Exercise Recommendation System Using Smart Watch," in Proc. 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIHI), 2023, doi: 10.1109/ICAIHI57871.2023.10489367. [5] UCI Machine Learning Repository, "CDC Diabetes Health Indicators," Dataset ID 891, doi: 10.24432/C53919. [Online]. Available: https://archive.ics.uci.edu/dataset/891/cdc+diabetes+health+indicators. Accessed: Oct. 21, 2026. [6] F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [Online]. Available: https://www.jmlr.org/papers/v12/pedregosa11a.html. [7] R. Smith, "An Overview of the Tesseract OCR Engine," in Proc. Ninth International Conference on Document Analysis and Recognition (ICDAR), 2007, pp. 629–633, doi: 10.1109/ICDAR.2007.4376991. [8] N. Reimers and I. Gurevych, "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks," in Proc. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, pp. 3982–3992, doi: 10.18653/v1/D19-1410. [9] P. Lewis et al., "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," in Advances in Neural Information Processing Systems, vol. 33, pp. 9459–9474, 2020. [10] L. M. Amugongo, P. Mascheroni, S. Brooks, S. Doering, and J. Seidel, "Retrieval augmented generation for large language models in healthcare: A systematic review," PLOS Digital Health, vol. 4, no. 6, e0000877, 2025, doi: 10.1371/journal.pdig.0000877.

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Publication Date

10/26/2026

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Double-Blind Peer Review

Review Rounds: 2

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