Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

Table 1

Summary of the results obtained by the related works.

Authors	Feature selection	Results of the evaluated metrics
Chou, Hsu, and Chou	No	(i) Accuracy: 0.80 to 0.95; (ii) Precision: 0.73 to 0.92; (iii) Recall: 0.62 to 0.93; (iv) score: 0.67 to 0.92; (v) AUC: 0.87 to 0.99
Kangra and Singh	No	(i) Accuracy: 0.66 to 0.98; (ii) Precision: 0.65 to 0.98; (iii) Recall: 0.65 to 0.98; (iv) AUC: 0.64 to 0.99; (v) MCC: 0.30 to 0.97; (vi) Kappa value: 0.29 to 0.98
Alcalá-Rmz, Zanella-Calzada, Galván-Tejada et al.	No	(i) Accuracy: 0.94 to 0.98; (ii) Loss function: 0.19 to 0.25; (iii) AUC: 0.98
Hu, Li, Lu et al.	Multiview subspace clustering guided	(i) AUC: 0.82
Haq, Li, Khan et al.	Decision tree (ID3), AdaBoost, and random forest	(i) Accuracy: 0.98 to 0.99; (ii) Recall: 0.98 to 1.00; (iii) Specificity: 0.97 to 0.99; (iv) Sensitivity: 0.98 to 1.00; (v) Precision: 0.99 to 1.00; (vi) MCC: 0.97 to 0.99; (vii) score: 0.98 to 1.00; (viii) ROC curve: 0.98 to 0.99
Sneha and Gangil	Correlation value	(i) Specificity: 0.98; (ii) Accuracy: 0.82
Olabanjo, Wusu, and Mazzara	Ensemble feature selection	(i) score: 0.69 to 1.00; (ii) Recall: 0.69 to 0.92; (iii) Precision: 0.83 to 1.00