Abstract :

Heart Disease Liver-related Conditions, And Brain Stroke Remain Among The Primary Causes Of Global Mortality, Largely Due To Limitations In Early And Accurate Diagnosis. Traditional Clinical Assessment Methods Depend Heavily On Manual Analysis Of Patient Records And Medical Images, Which Often Results In Diagnostic Delays And Increased Health Risks. To Address These Issues, This Paper Proposes An Intelligent Disease Diagnosis Framework That Integrates Machine Learning (ML) And Deep Learning (DL) Techniques. The System Performs Prediction Of Heart Disease, Liver Disease, And Brain Stroke Using A Combination Of Structured Clinical Parameters And Medical Imaging Data. Machine Learning Algorithms Process Numerical Clinical Features Such As ECG Readings And Laboratory Test Results, While Convolutional Neural Networks(CNNs) Analyze CT And MRI Images For Automated Stroke Identification. The Proposed Framework Delivers Accurate And Efficient Predictions, Reduces Manual Diagnostic Effort, And Supports Clinicians In Timely Decision-making. Experimental Results Demonstrate Consistent And Reliable Predictive Performance, Confirming The Effectiveness Of The Proposed Approach For Early Disease Detection And Intelligent Healthcare Assistance. Index Terms— Machine Learning, Deep Learning, Heart Disease Prediction, Liver Disease Diagnosis, Brain Stroke Detection, Convolutional Neural Network, Medical Decision Support, Healthcare Analytics.

Published:

02-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

135-144

Section:

Articles

License:

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

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