AUTOMATIC LEARNING OF RULES - A PRACTICAL EXAMPLE OF USING ARTIFICIAL-INTELLIGENCE TO IMPROVE COMPUTER-BASED DETECTION OF MYOCARDIAL-INFARCTION AND LEFT-VENTRICULAR HYPERTROPHY IN THE 12-LEAD ECG

The authors developed a computer program that detects myocardial infar ction (MI) and left ventricular hypertrophy (LVH) in two steps: (1) by extracting parameter values from a 10-second, 12-lead electrocardiogr am, and (2) by classifying the extracted parameter values with rule se ts. Every disease has its dedicated set of rules. Hence, there are sep arate rule sets for anterior MI, inferior MI, and LVH. If at least one rule is satisfied, the disease is said to be detected. The computer p rogram automatically develops these rule sets. A database (learning se t) of healthy subjects and patients with MI, LVH, and mixed MI + LVH w as used. After defining the rule type, initial limits, and expected qu ality of the rules (positive predictive value, minimum number of patie nts), the program creates a set of rules by varying the limits. The ge neral rule type is defined as: disease = lim(11) < p(1) less than or e qual to lim(1u), and lim(2l) < p(2) less than or equal to lim(2u) and ... lim(nl) < p(n) less than or equal to lim(nu). When defining the ru le types, only the parameters (p(1)...p(n)) that are known as clinical electrocardiographic criteria (amplitudes [mV] of Q, R, and T waves a nd ST-segment; duration [ms] of Q wave; frontal angle [degrees]) were used. This allowed for submitting the learned rule sets to an independ ent investigator for medical verification. It also allowed the creatio n of explanatory texts with the rules. These advantages are not offere d by the neurons of a neural network. The learned rules were checked a gainst a test set and the following results were obtained: MI: sensiti vity 76.2%, positive predictive value 98.6%; LVH: sensitivity 72.3%, p ositive predictive value 90.9%. The specificity ratings for MI are bet ter than 98%; for LVH, better than 90%.