Data Mining for Heart Disease Prediction Based on Echocardiogram and Electrocardiogram Data

Traditional methods of detecting cardiac illness are often problematic in the medical field. The doctor must next study and interpret the findings of the patient's medical record received from the electrocardiogram and echocardiogram. These tasks often take a long time and require patience. The use of computational technology in medicine, especially the study of cardiac disease, is not new. Scientists are continuously striving for the most reliable method of diagnosing a patient's cardiac illness, particularly when an integrated system is constructed. The study attempted to propose an alternative for identifying cardiac illness using a supervised learning technique, namely the multi-layer perceptron (MLP). The study started with the collection of patient medical record data, which yielded up to 534 data points, followed by pre-processing and transformation to provide up to 324 data points suitable to be employed by learning algorithms. The last step is to create a heart disease classification model with distinct activation functions using MLP. The degree of classification accuracy, k-fold cross-validation, and bootstrap are all used to test the model. According to the findings of the study, MLP with the Tanh activation function is a more accurate prediction model than logistics and Relu. The classification accuracy level (CA) for MLP with Tanh and k-fold cross-validation is 0.788 in a data-sharing situation, while it is 0.672 with Bootstrap. MLP using the Tanh activation function is the best model based on the CA level and the AUC value, with values of 0.832 (k-fold cross-validation) and 0.857 (bootstrap).Prediction of heart disease is a complicated thing when done with traditional medical analysis. Sometimes the prediction results are not as expected and even fail. The development of computational science offers a number of alternatives to help predict the type of heart disease based on the availability of patient medical record data. One alternative that can be used is data mining. The goal of this study is to implementing data mining to predict the type of heart disease based on medical record data from echocardiograms and electrocardiograms. Multilayer perceptron backpropagation (MLP) is used in this study. The results show that MLP with the Tanh activation function is a better predictive model than logistic and Relu. The classification accuracy level (CA) for MLP with Tanh is 0.788 for a data sharing scenario using k-fold cross validation and 0.672 for a data sharing scenario using Bootstrap. From both the CA level and the AUC value, MLP with the Tanh activation function is the best model with a value of 0.832 (k-fold cross validation) and 0.857 (bootstrap)