Heart Disease Prediction Using Binary Classification

In this project, I built a neural network model to predict heard disease with binary classification technique using patient information dataset from UCI Machine Learning repository. This dataset was preprocessed to remove missing elements and performed feature extraction. Our result shows that the model that I built has the best performance accuracy in heart disease classification if compared to other models and algorithms. The model achieved 94.98% accuracy after hyperparameter tuning and 0.947 area under the curve in ROC curve analysis. In addition, to identify the most important factors in heart disease prediction, I also performed feature importance analysis. Our analysis showed that factors such as type of chest pain, peak heart rate, and exercise-induced ST-segment depression were among the strongest predictors of heart disease. Overall, the project demonstrated the effectiveness of neural network models in medical diagnosis and provided insights into heart disease classification. The model developed can be used as a decision support tool for healthcare professionals in planning the diagnosis and treatment of heart disease. However, further research is needed to confirm the model's performance in larger and more diverse patient populations.