An Efficient Machine Learning and Deep Learning Framework for Early Prediction and Risk Assessment of Chronic Diseases

Abstract

Timely identification of chronic illnesses is imperative in minimizing death rates, enhancing patient care, and facilitating early clinical treatment. This paper presents an effective machine learning and deep learning framework to early-stage predict the Chronic Kidney Disease (CKD) and chronic heart disease using structured clinical data available in the UCI Machine Learning Repository. The main aim of the study is to establish the perfect predictive models combined with the risk evaluation and personalized recommendation systems in the context of intelligent healthcare support.The suggested methodology will involve data preprocessing, missing value imputation, feature scaling, exploratory data analysis, feature selection and stratified data splitting. Several machine learning models were used to predict CKD including Naive Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Support Vector Machine models, and random forest models and Hybrid Ensemble models. Moreover, a more sophisticated CNN-UDRP deep learning network with embedding layers, convolutional neural network, and latent feature extraction via PCA was created. Deep ANN and Advanced DNN with LeakyReLU architectures were applied to predict chronic heart diseases with the help of batch normalization and dropout regularization.Experimental findings prove that the Random Forest model provided the highest performance of CKD prediction with the highest accuracy, precision, recall and F1-score of 100, whereas the Advanced DNN with LeakyReLU provided the highest accuracy of heart disease prediction at 99.93. The suggested framework also incorporates risk stratification and individual health recommendations, thus it can be used in scalable and intelligent healthcare decision-support systems.