Application of Artificial Neural Network Algorithms to Heart Disease Prediction Models with Python Programming - Yuda Syahidin; Aditya Pratama Ismail; Fawwaz Nafis Siraj (Jurnal E-Komtek (Elektro-Komputer-Teknik), 2022)
Heart disease is one of the deadliest diseases in and is the number one killer in the world so many studies are carried out to contribute to predicting a person's heart disease. This study aims to help create an early heart disease prediction model from the UCI Machine Learning Repository dataset. The method proposed in this study is a deep learning technique that applies an artificial neural network algorithm with a hidden layer technique in making a heart disease prediction model. This research stage found problems in improving the accuracy of the datasets used by dealing with problems in pre-processing data, such as missing data and determining the form of data correlation. The model was then tested through a heart disease dataset and yielded 90% accuracy. With the creation of this prediction model with python programming, it is hoped that in addition to helping to make disease predictions, it can also provide further innovations in data science in the health sector.