ABSTRACT
Objective To develop a mobile app called “TMT Predict” to predict the results of Treadmill Test (TMT), using data mining techniques applied to a clinical dataset using minimal clinical attributes. To prospectively test the results of the app in realtime to TMT and correlate with coronary angiogram results. Methods In this study, instead of statistics, data mining approach has been utilized for the prediction of the results of TMT by analyzing the clinical records of 1000 cardiac patients. This research employed the Decision Tree algorithm, a new modified version of K-Nearest Neighbor (KNN) algorithm, K-Sorting and Searching (KSS). Furthermore, curve fitting mathematical technique was used to improve the Accuracy. The system used six clinical attributes such as age, gender, body mass index (BMI), dyslipidemia, diabetes mellitus and systemic hypertension. An Android app called “TMT Predict” was developed, wherein all three inputs were combined and analyzed. The final result is based on the dominating values of the three results. The app was further tested prospectively in 300 patients to predict the results of TMT and correlate with Coronary angiography. Results The accuracy of predicting the result of a TMT using data mining algorithms, Decision Tree and K-Sorting & Searching (KSS) were 73% and 78%, respectively. The mathematical method curve fitting predicted with 82% accuracy. The accuracy of the mobile app “TMT Predict”, improved to 84%. Age-wise analysis of the results show that the accuracy of the app dips when the age is more than 60 years indicating that there may be other factors like retirement stress that may have to be included. This gives scope for future research also. In the prospective study, the positive and negative predictive values of the app for the results of TMT and coronary angiogram were found to be 40% and 83% for TMT and 52% and 80% for coronary angiogram. The negative predictive value of the app was high, indicating that it is a good screening tool to rule out coronary artery heart disease (CAHD). Conclusion “TMT Predict” is a simple user-friendly android app, which uses six simple clinical attributes to predict the results of TMT. The app has a high negative predictive value indicating that it is a useful tool to rule out CAHD. The “TMT Predict” could be a future digital replacement for the manual TMT as an initial screening tool to rule out CAHD.