The foundation has collected data from 303 patients (164 negative and 139 positive) in the last 10 years with symptoms of heart diseases. The following Excel sheet[1] have listed the attributes used in the dataset as follows

1. age 2. Sex (1=male, 0=female) 3. chest_pain (1=typical, 2=atypical, 3=non-angina,4=asymptomatic) 4. Rest_bp (resting blood pressure) 5. cholestoral (cholesterol level mg/dl) 6. Fast_bsugar (fasting blood sugar level >120; 1=true, 0=false) 7. electrocardio (electrocardiographic results) 8. Max_hear_rate (max heart rate achieved) 9. Exer_angina (exercised induced angina) 10. Dep_exer (depression induced by exercise) 11. Slope_exer (slope of the peak of exercise) 12. Major_vessel (number of major vessels) 13. thal (3=normal, 6=fixed defect, 7=reversable defect) 14. diagnosis (the predicted attribute) (positive, negative)

3. Draw 1 table highlighting the performance of the following classifiers (RIPPER, PART, Decision Table, Random Forest, J48, Random Tree, Artificial Neural Network, Simple Logistics, and Nave Bayes).

In this table, highlight and group the different classifier types (i.e., bayes, functions, trees, and rule based). Show the following performance measures for your evaluations: Accuracy, Sensitivity/Recall, Precision, F-Measure, and ROC Area. (5 Marks)

4. Analyze the results in question 3 above. Explain in detail the performance of the above classifiers by comparing the classifier types (3 Marks). Select 1 classifier that is better suited for the dataset, that you wish to recommend, based on what measure(s) and why. (2 Marks)

5. Run a cluster analysis algorithm (simple k-means) on the dataset. Did the algorithm do a better job in clustering the dataset given that we know the predicted attribute (i.e., positive and negative patients)? Explain your answer based on the results. (3 Marks)

[1]David W. Aha & Dennis Kibler. Instance-based prediction of heart-disease presence with the Cleveland database.

Data collected by Dr. Andras Janosi, M.D., Dr. William Steinbrunn, M.D., Dr. Matthias Pfisterer, M.D., and Dr. Robert Detrano, M.D., Ph.D.