Cumulative gain and lift charts for model performance assessment in mineral potential mapping

Model performance assessment is a key procedure for mineral potential mapping, but the correspond-ing research achievements are seldom reported in literature. Cumulative gain and lift charts are well known in the data mining community specialized in marketing and sales applications and widely used in customer chum prediction for model performance assessment. In this paper, they are introduced into the field of mineral poten-tial mapping for model performance assessment. These two charts can be viewed as a graphic representation of the advantage of using a predictive model to choose mineral targets. A cumulative gain curve can represent how much a predictive model is superior to a random guess in mineral target prediction. A lift chart can express how much more likely the mineral targets predicted by a model are deposit-bearing ones than those by a random se-lection. As an illustration, the cumulative gain and lift charts are applied to measure the performance of weights of evidence, logistic regression,restricted Boltzmann machine, and multilayer perceptron in mineral potential mapping in the Altay district in northern Xinjiang in China. The results show that the cumulative gain and lift charts can visually reveal that the first three models perform well while the last one performs poorly. Thus, the cumulative gain and lift charts can serve as a graphic tool for model performance assessment in mineral potential mapping.

A Machine Learning Classification Model for Detecting Prediabetes

The incidence of prediabetes is in a dangerous condition in the USA. The likelihood of increasing chronic and complex health issues is very high if this stage of prediabetes is ignored. So, early detection of prediabetes conditions is critical to decrease or avoid type 2 diabetes and other health issues that come as a result of untreated and undiagnosed prediabetes condition. This study is done in order to detect the prediabetes condition with an artificial intelligence method. Data used for this study is collected from the Centers for Disease Control and Prevention’s (CDC) survey conducted by the Division of Health and Nutrition Examination Surveys (DHANES). In this study, several machine learning algorithms are exploited and compared to determine the best algorithm based on Average Squared Error (ASE), Kolmogorov-Smirnov (Youden) scores, areas under the ROC and some other measures of the machine learning algorithm. Based on these scores, the champion model is selected, and Random Forest is the champion model with approximately 89% accuracy.