Authors - Taeko Onodera, Koutaro Hachiya, Yuhei Hatakenaka Abstract - Numerous studies have applied machine learning to diagnosis and screening in the medical and welfare fields. However, it is rare for the resulting machine learning models to be widely adopted in clinical practice. This study proposes a method for deriving diagnostic rules from machine learning models that can be applied manually without the use of computers. The proposed method involves inputting all possible patterns into a trained model, generating a truth table with the corresponding prediction results, and then using the Quine– McCluskey method to derive logical expressions that serve as manual diagnostic rules. In the experiments, the proposed method was compared with conventional methods for deriving manual diagnostic rules from datasets: the point score system, a method based on likelihood ratios, and a logic derivation method based on rough set theory. Only the proposed method achieved a positive clinical utility index of 0.81 or higher—classified as “excellent”—even when the number of rules was limited to just two or three.
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