Personalized Healthcare Insights AI Driven Predictive Analysis and Intervention

Abstract

The increasing prevalence of chronic diseases such as lung cancer, heart disorders, and neurological conditions has created a growing need for AI-based systems that can support early detection and timely intervention. In this study, a ResNet50 (CNN) model is utilized for classifying lung diseases using X-ray and CT scan images, achieving high accuracy in identifying abnormalities. For heart disease prediction, Random Forest and Support Vector Machine (SVM) algorithms are used to analyze structured medical data, including ECG readings, cholesterol levels, and blood pressure. A hybrid CNN-SVM approach is also implemented for brain disease detection, where CNN extracts key features from MRI and CT scans, while SVM enhances classification precision. The dataset undergoes several preprocessing stages such as image normalization, data augmentation, and feature extraction to strengthen model performance. Training is performed using cross-entropy loss with an Adam optimizer, along with early stopping techniques to minimize overfitting. The proposed system achieves improved classification accuracy, supporting proactive diagnosis and personalized treatment planning. By combining multiple machine learning models tailored to various diseases, the system provides a reliable predictive framework. Future developments will focus on hybrid deep learning methods, larger datasets, and real-time clinical applications to improve accuracy, scalability, and overall effectiveness in healthcare prediction systems.

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