Abstract :

Predictive Healthcare Analytics Is An Advanced Medical Decision-support System That Utilizes Artificial Intelligence (AI) And Machine Learning (ML) Techniques To Diagnose Diseases From Patient Medical Records. The Proposed System Is Implemented Using Python And Streamlit, Integrating Supervised Learning Algorithms Such As Decision Tree, Random Forest, And Support Vector Machine (SVM). The System Processes Structured Medical Datasets Containing Symptoms As Input Features And Disease Labels As Output Classes. After Preprocessing And Encoding, The Dataset Is Split Into Training And Testing Sets. Multiple ML Models Are Trained And Evaluated Using Performance Metrics Such As Accuracy, Precision ,Recall ,F1-score, And ROC Curve. The Application Provides An Interactive Web Interface Where Users Can Select Symptoms And Obtain Real-time Disease Predictions From Multiple Models. The System Also Compares Model Performances Visually, Helping Identify The Best-performing Classifier. This Approach Enhances Diagnostic Accuracy, Reduces Manual Effort, And Supports Early Disease Detection Using Intelligent Analytics. Keywords : Artificial Intelligence, Machine Learning, Disease Prediction, Healthcare Analytics, Decision Tree, Random Forest, Support Vector Machine (SVM), Medical Data Analysis, Predictive Healthcare, Streamlit.

Published:

28-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

865-870

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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