Healthcare AI

Ritik Raj Srivastav, Harsh Gautam, Ritik Kumar, Gaurav Tomar and Kajal Kori

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 2, 885-891

Abstract: Inadequate access to timely healthcare guidance, especially in rural and underserved areas, contributes to delayed diagnosis and preventable health complications. This paper presents Healthcare AI, a comprehensive machine learning- based framework that integrates disease prediction, medicine recommendation, personalized diet planning, and daily routine generation into a unified web-based application. The system accepts multi- factor inputs including patient symptoms, age, BMI, body temperature, and comorbidities and leverages three supervised classification algorithms — Logistic Regression, Decision Tree, and Random Forest — trained on curated symptom-disease datasets. Preprocessing includes binary symptom encoding, StandardScaler normalization, mutual information-based feature selection, and PCA dimensionality reduction retaining 95% variance. The best-performing model is automatically selected based on test accuracy. Experimental evaluation achieved a classification accuracy of up to 95.1%, with sub-second prediction latency. The modular Streamlit-based interface supports five functional modules: disease prediction with confidence scoring, medicine recommendations with dosage and contraindication guidance, condition-specific diet plans, daily health routines, and an interactive analytics dashboard. The system is designed as an assistive tool to support informed preliminary health decisions.

Keywords: Machine Learning; Disease Prediction; Medicine Recommendation; Diet Planning; Healthcare AI; Random Forest; Logistic Regression; Decision Tree; Streamlit; PCA; Feature Selection (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i2:id:1530

DOI: 10.32628/IJSRST2613358

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