EARLY PREDICTION OF HEART DISEASE USING DEEP BELIEF-ASSISTED NEURAL PREDICTOR (DBANP) MODEL

Heart Disease is considered to be the one of the major diseas.Nowadays it makes the loss of numerous lives in our country. For this we need to predict the diseases. The Deep Belief-Assisted Neural Predictor (DBANP) model is proposed in this study to increase the accuracy of heart disease prediction. The model overcomes the drawbacks of previous models like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), which frequently suffer from vanishing gradients and lengthy training times, by combining an ANN for initial feature extraction with a DBN for capturing intricate hierarchical dependencies. Models were developed and evaluated using the Cleveland Heart Disease Dataset and the Cardiovascular Disease Dataset. To guarantee high-quality input data, extensive pre-processing was carried out, including managing missing values and feature selection. Using important measures including Accuracy, Precision, Recall, Specificity, F1 Score, AUC-ROC, and AUC-PR, the DBANP model was contrasted with RNN and LSTM models. According to experimental data, the suggested model performs noticeably better than current models, providing increased predictive power and resilience. By offering a hybrid deep learning system for early disease identification that is both scalable and flexible, this study advances predictive healthcare.