Published on: June 2026
SYMPTOM-DRIVEN DISEASE PREDICTION AND ADVISORY PLATFORM
Adnan Hashmi Daksh Khosla Priyanshu Singh
Prof. Jaibheem Gaikwad
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Abstract
This project presents a compact, maintainable, and explainable system that maps self-reported symptoms to likely disease labels and returns curated, non-prescriptive guidance to the user. The system uses a deterministic symptom indexing approach to convert free-text symptom inputs into fixed-length binary feature vectors.
A multi-class Support Vector Classifier (SVC) with a linear kernel is trained on a curated dataset of symptom-disease mappings and serialized for fast inference. Auxiliary content (disease descriptions, precautions, educational medication pointers, diet and activity advice) is stored in editable CSVs to allow healthcare experts to update guidance without modifying code.
The backend is implemented in Flask; the frontend is responsive and optimized for clarity and safety messaging. Model evaluation shows high aggregate performance on the available test split, but the report emphasizes responsible usage, limitations, and recommended next steps for clinical validation.
How to Cite this Paper
Hashmi, A., Khosla, D. & Singh, P. (2026). Symptom-Driven Disease Prediction and Advisory Platform. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.145
Hashmi, Adnan, et al.. "Symptom-Driven Disease Prediction and Advisory Platform." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.145.
Hashmi, Adnan,Daksh Khosla, and Priyanshu Singh. "Symptom-Driven Disease Prediction and Advisory Platform." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.145.
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Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Jun 12 2026
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