Computational and Mathematical Methods in Medicine / 2022 / Article / Tab 9 / Research Article
Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework Table 9 Result of decision matrix.
Alternatives/criteria Performance evaluation metric criteria Hybrid diagnosis models C1 C2 C3 C4 C5 C6 C7 A1 ReF-decision tree 0.37700 0.00001 0.99312 0.98943 0.98943 0.98944 0.98943 A2 ReF-SVM 2.22900 0.91700 0.95052 0.83245 0.83251 0.83266 0.83245 A3 ReF-naive Bayes 0.13400 0.05000 0.96142 0.84415 0.84367 0.84344 0.84415 A4 ReF-KNN 0.68700 0.51000 0.90306 0.76038 0.74901 0.75549 0.76038 A5 ReF-AdaBoost 0.57700 0.28700 0.98897 0.98528 0.98528 0.98530 0.98528 A6 IG-decision tree 0.47200 0.00200 0.99312 0.98943 0.98943 0.98944 0.98943 A7 IG-SVM 2.70600 0.91400 0.94778 0.83358 0.83364 0.83401 0.83358 A8 IG-naive Bayes 0.21900 0.04000 0.96313 0.84113 0.84029 0.83988 0.84113 A9 IG-KNN 0.60900 0.60600 0.89653 0.75585 0.74503 0.74750 0.75585 A10 IG-AdaBoost 0.69700 0.37600 0.98642 0.98189 0.98189 0.98193 0.98189 A11 Chi2 -decision tree 0.46600 0.00200 0.99271 0.98830 0.98830 0.98830 0.98830 A12 Chi2 -SVM 3.19100 1.06000 0.94979 0.83094 0.83080 0.83066 0.83094 A13 Chi2 -naive Bayes 0.22900 0.04300 0.96235 0.84075 0.83966 0.83926 0.84075 A14 Chi2 -KNN 0.90700 0.62900 0.89462 0.74302 0.73201 0.73414 0.74302 A15 Chi2 -AdaBoost 0.75400 0.59500 0.98670 0.98226 0.98226 0.98230 0.98226
C: criteria; A: alternative; C1: train time; C2: test time; C3: AUC; C4: classification accuracy; C5: F1 score; C6: precision; C7: recall.