Engineering

"For this task, I have selected the ""Heart Disease UCI"" dataset from the UCI Machine Learning Repository. The dataset contains 14 attributes, including age, sex, chest pain type, blood pressure, serum cholesterol levels, fasting blood sugar, electrocardiographic results, maximum heart rate achieved, exercise-induced angina, ST depression induced by exercise relative to rest, slope of the peak exercise ST segment, number of major vessels colored by fluoroscopy, and thalassemia.

3.2 The problem solved in the selected paper ""Predicting Heart Disease Using Machine Learning Techniques: A Comparative Study"" by K. Sathya et al. is to predict the presence of heart disease based on the given attributes. The paper uses various machine learning algorithms, such as decision trees, K-nearest neighbors, support vector machines, and artificial neural networks, to predict heart disease. The goal is to determine the best-performing algorithm in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC)."