LAUSR

Machine learning is the field of computer science that deals with algorithms to make predictions and learn from the data without being explicitly programmed. Simply stated, the overarching idea is to design algorithms that don’t anticipate instructions, but reason themselves. They can understand both linear and non-linear relationships in the data and find patterns that would be otherwise cumbersome to obtain. Smart phone applications (apps) based on machine learning algorithms are opening new avenues in the field of cardiology to improve both work-flow efficiency and patient care. There are several emerging mobile phone apps available for predicting different cardiovascular diseases. For example, a readily available app “MESA Score” uses coronary artery calcium score and traditional risk factors to predict the 10-year coronary heart disease risk in a patient. Along similar lines, Amutha and colleagues, in this issue of Indian Heart Journal, explored the development and validation of an android mobile app, which gives a pretest probability to rule out coronary artery disease (CAD) by predicting the results of treadmill test (TMT) in patients without performing the TMT physically. Authors used a training set of seven hundred and fifty patients, a test set of two hundred and fifty patients and a validation set of 300 patients. They used three machine learning algorithms to predict the results of TMT based on six basic features, namely, age, gender, BMI, dyslipidemia, diabetes Mellitus, and systemic hypertension. Authors reported relatively high specificity in their models of 91% with lower sensitivity of 69%. CAD is extremely prevalent in the South East Asian countries where any effort to provide a cost-effective strategy to improve the screening of patients at risk is certainly welcome. The use of data mining methods is particularly appealing as these techniques are preferable over common statistical methods for making predictions. The app presented by Amutha and colleagues, however, had lower diagnostic yield in prospective three hundred patients group as compared to two hundred and fifty test patients. Interestingly, their data also predicted the results of coronary angiograms with a specificity of 80% and negative predictive value of 80%; however, the sensitivity was diminutive (35%). This may be related to variations in population or disease features and questions the overall generalizability of the test results. In addition, data mining approaches are “data driven”, since these rely principally on input data rather than model based, where a model is build following empirical or theory-based principles. For data driven algorithms, it is important to give a statistical description of the input dataset since the presence of bias or uncommon patterns can make the