Target classification using machine learning approaches with applications to clinical studies

Machine learning has been a trending topic for which almost every research area would like to incorporate some of the technique in their studies. In this paper, we demonstrate several machine learning models using two different data sets. One data set is the thermograms time series data on a cancer study that was conducted at the University of Louisville Hospital, and the other set is from the world-renowned Framingham Heart Study. Thermograms can be used to determine a patient’s health status, yet the difficulty of analyzing such a high-dimensional dataset makes it rarely applied, especially in cancer research. Previously, Rai et al.1 proposed an approach for data reduction along with comparison between parametric method, non-parametric method (KNN), and semiparametric method (DTW-KNN) for group classification. They concluded that the performance of two-group classification is better than the three-group classification. In addition, the classifications between types of cancer are somewhat challenging. The Framingham Heart Study is a famous longitudinal dataset which includes risk factors that could potentially lead to the heart disease. Previously, Weng et al.2 and Alaa et al.3 concluded that machine learning could significantly improve the accuracy of cardiovascular risk prediction. Since the original Framingham data have been thoroughly analyzed, it would be interesting to see how machine learning models could improve prediction. In this manuscript, we further analyze both the thermogram and the Framingham Heart Study datasets with several learning models such as gradient boosting, neural network, and random forest by using SAS Visual Data Mining and Machine Learning on SAS Viya. Each method is briefly discussed along with a model comparison. Based on the Youden’s index and misclassification rate, we select the best learning model. For big data inference, SAS Visual Data Mining and Machine Learning on SAS Viya, a cloud computing and structured statistical solution, may become a choice of computing.