Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models

Table 1

Specificity and sensitivity test scores.

Model	FNR (%)	TNR (%)	FPR (%)	PPV (%)	NPV (%)	TPR (%)
Gradient boosting	0.23	16.67	83.33	98.87	50.0	99.77
Logistic regression	0.08	16.67	83.33	98.87	75.0	99.92
Random forest	0.53	11.11	88.89	98.79	22.22	99.47
Support vector machine	0.08	5.56	94.44	98.72	50.0	99.92

The highlighted scores tell us how much trust to put into a test result. Can a negative test result be truly trusted or not. probability of trust in a test result. Negative predicted value is the ratio of patients truly predicted as defaulters to all patients diagnosed as defaulters. It is a probability estimate for a nondefault status if described as a default patient. High percentage value means that the probability for a prediction miss is lower (minimal error). Lower percentage means a very high probability for a prediction miss.