Research Article
Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models
Table 1
Specificity and sensitivity test scores.
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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. |