Healthcare AI
DOI:
https://doi.org/10.32628/IJSRST2613358Keywords:
Machine Learning, Disease Prediction, Medicine Recommendation, Diet Planning, Healthcare AI, Random Forest, Logistic Regression, Decision Tree, Streamlit, PCA, Feature SelectionAbstract
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.
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