Deep Learning-Based Predictive Modeling for Early Diagnosis and Prognosis of Diseases in Healthcare Systems

Abstract

Preclinical identification and prediction of the prognosis of chronic diseases in healthcare remains a major issue in the modern healthcare systems due to the fast increasing heterogeneous clinical data and constraints of the traditional diagnostic methods. Conventional machine learning and statistical prediction models tend to have lower predictive accuracy, low scalability and are less able to predict with complex nonlinear healthcare data. This paper presents a predictive modeling system that is based on deep learning and allows for the early diagnosis and prognosis of diseases in medical systems. The offered structure will combine healthcare data preprocessing, feature normalization, dimension reduction and the optimal deep neural network architecture to advance the performance of disease prediction and clinical decision support. Experimental evaluation was done using publicly available datasets of healthcare such as heart disease, diabetes, and chronic kidney disease datasets. The deep learning model was developed and trained with Adam optimizer and optimized hyperparameters and 10-fold cross-validated. Accuracy, precision, recall, F1-score and ROC-AUC measures were used to measure performance evaluation. The results of the experiments proved that the suggested framework was better than more traditional machine learning algorithms, including Support Vector Machine, K-Nearest Neighbor, and Random Forest classifiers. The model proposed had a high general prediction accuracy of 96.8 and a ROC-AUC measure of 0.978 with the benefit of lessening the false positive prediction and enhancing the generalization ability. The findings reveal how deep learning-based healthcare analytics can be effective in intelligent diagnosis of diseases, prediction of their prognosis, and the next-generation AI-enhanced healthcare systems.