Early Detection of Cardiovascular Disorders Using Enhanced ANN Model

This research paper presents a study that focuses on predicting heart disease using an Artificial Neural Network (ANN), with Logistic Regression serving as the reference model. The study utilizes a dataset containing indicators of heart disease. An ANN model is then trained on the training data to predict heart disease in the testing data. To evaluate the model's performance several metrics are employed, including a confusion matrix and a classification report. The model proposed by previous literature has achieved an accuracy rate of 85.71% for Logistic Regression-a used method for predicting heart disease. However, our ANN model surpasses this baseline by achieving an accuracy rate of 94.15%. The paper provides an analysis of our methodology encompassing data preprocessing, cross-validation, model construction, training procedures well as evaluation techniques. The results underscore the capabilities of ANN in predicting heart disease signifying an important avenue for future research, in this field.