AI-Driven Early Disease Prediction System using Hybrid Machine Learning and Deep Learning Models

RASIPOGULA PAVAN KUMAR
M-Tech, Department .Of Computer Science And Engineering,
Vemu Institute Of Technology,
P.Kothakota,Chittoor District, Andhra Pradesh-517112,India
Email Id: justmailtopavankumar@gmail.com
Mrs. P BINDUMADHAVI
Assistant professor, M.Tech,Dept of CSE,
Vemu Institute Of Technology,
P.Kothakota,Chittoor District, Andhra Pradesh-517112,India
Email Id: bindupulivarthi994@gmail.com

Abstract - Early disease prediction is a significant aspect of modern preventive care and facilitates early medical intervention, which is beneficial for patient health. Nevertheless, early-disease prediction is still very difficult to achieve  because of the diverse nature of patient data, the intricate disease development patterns, and the large number of analyzed features in clinical datasets. In this paper, we propose an AI-Driven Early Disease Prediction System based on a Hybrid Machine Learning (ML) and Deep Learning (DL) models with the best predictive accuracy for multiple disease classes. The proposed framework consists of a two-model approach: classical ML algorithms (RF, XGBoost, and SVM) in better extraction of low-level features and DL models (CNN and LSTM) for understanding high-level feature representations. A dynamic ensemble fusion strategy generalizes model contributions based on data modality and disease information. Thesystem is tested on standard public datasets like the UCI Heart Disease, PIMA Indian Diabetes and the MIMIC-III clinical database. The experimental results show that the accuracies of classification for heart disease, diabetes, and the multi disease prediction are 96.4%, 94.7%, and 91.2% respectively, which achieve superior performance than state-of-the-art methods. Ablation studies and cross-validation tests further corroborate the robustness and generalizability of the proposed model.
Keywords - Early disease prediction; hybrid machine learning; deep learning; CNN-LSTM; ensemble learning; clinical data analysis; healthcare AI.