Jerline Amutha A, Padmajavalli R, Prabhakar D
Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, India; Assistant Professor, Department of Computer Science, Women's Christian College, Chennai, India.
Head, Department of Computer Applications, Bhaktavatsalam Memorial College for Women, Korattur, Chennai, India; Research Supervisor, Department of Computer Science, Bharathiar University, Coimbatore, Chennai, India.
Indian Heart J. 2018 Jul-Aug;70(4):511-518. doi: 10.1016/j.ihj.2018.01.011. Epub 2018 Jan 8.
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.
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.
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 60years 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).
"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.
开发一款名为“TMT Predict”的移动应用程序，利用数据挖掘技术，基于最少的临床属性对临床数据集进行分析，以预测平板运动试验（TMT）的结果。对该应用程序的结果与TMT的实时结果进行前瞻性测试，并与冠状动脉造影结果进行关联。
在本研究中，采用数据挖掘方法而非统计学方法，通过分析1000例心脏病患者的临床记录来预测TMT结果。本研究采用了决策树算法、一种新的改进版K近邻（KNN）算法、K排序与搜索（KSS）算法。此外，使用曲线拟合数学技术来提高准确率。该系统使用了六个临床属性，如年龄、性别、体重指数（BMI）、血脂异常、糖尿病和系统性高血压。开发了一款名为“TMT Predict”的安卓应用程序，其中将所有三个输入进行组合和分析。最终结果基于三个结果中的主导值。该应用程序在300例患者中进行了前瞻性测试，以预测TMT结果并与冠状动脉造影结果进行关联。
使用数据挖掘算法、决策树和K排序与搜索（KSS）预测TMT结果的准确率分别为73%和78%。数学方法曲线拟合的预测准确率为82%。移动应用程序“TMT Predict”的准确率提高到了84%。按年龄进行的结果分析表明，当年龄超过60岁时，该应用程序的准确率会下降，这表明可能还需要纳入其他因素，如退休压力等。这也为未来的研究提供了空间。在前瞻性研究中，该应用程序对TMT和冠状动脉造影结果的阳性预测值和阴性预测值分别为：TMT的阳性预测值为40%，阴性预测值为83%；冠状动脉造影的阳性预测值为52%，阴性预测值为80%。该应用程序的阴性预测值较高，表明它是排除冠状动脉心脏病（CAHD）的良好筛查工具。
“TMT Predict”是一款简单易用的安卓应用程序，它使用六个简单的临床属性来预测TMT结果。该应用程序具有较高的阴性预测值，表明它是排除CAHD的有用工具。“TMT Predict”可能会成为未来手动TMT的数字替代品，作为排除CAHD的初始筛查工具。
