Problem 1. Classification - Confusion matrix and KNN (15 points) A sample data set of patients at a Cardiac clinic located in a district is given below. It is costly do a detailed diagnostic check on the patients for heart disease. The doctor wants to develop a rule to classify the patient as potential heart patient or not, using variables like Gender and Weight and using data mining methods like Bayesian and KNN. Use the given information to predict whether a patient is a candidate for heart disease. The weight (in pounds) attribute be divided into four ranges (0, 125], (125, 150], (150, 200], (200, 0) Weight Height Name Gender (pounds) (m) Output 1 Output 2 1 Kat F 125 1.5 No Disease Disease 2 Stephanie F 130 1.55 No Disease Disease 3 Kathy F 135 1.6 No Disease No Disease 4 Diane F 140 1.6 No Disease Disease 5 Lynette F 160 1.65 No Disease Disease 6 Debbie F 135 1.55 Disease No Disease 7 Amy F 160 1.6 Disease No Disease 8 Mary F 170 1.7 Disease No Disease 9 Dolly F 180 1.8 Disease Disease 10 Natasha F 160 1.7 Disease No Disease 11 Annan M 125 15 1.5 No Disease No Disease 12 Annan M 140 1.6 No Disease No Disease 13 Todd M 150 1.65 No Disease No Disease 14 Jim M 185 1.8 No Disease Disease 15 John M 210 2 No Disease No Disease 16 Steve M 160 1.65 Disease Disease 17 Worth M 165 1.7 Disease Disease 18 Johnson M 195 1.95 Disease Disease 19 Stevenson M 220 4 Disease Disease 20 20 Wellington M 230 4 Disease No Disease