Related Experiment Videos
Decision tree with randomized grid search-based hyperparameter tuning and optimal feature scaling for diabetes
Mudatheer M Al-Slivani1, Ibrahim O Alrubaye2, Mayameen S Kadhim3
1Department of Physics, College of Education for Pure Sciences, Al-Furqan University, Mosul, Iraq.
Background:
Diabetes is a chronic condition that arises when the body cannot effectively regulate blood glucose levels, either due to insufficient insulin production or insulin resistance. If left unmanaged, diabetes can lead to serious complications including heart disease, nerve damage, kidney failure, and blindness. Machine learning classifiers are widely employed to predict diabetes onset based on patient data.
Methods:
This paper presents a robust data-driven machine learning framework for enhancing the classification of diabetes. Unlike previous decision tree models that employed the classic grid search method, we propose a decision tree (DT) model utilizing randomized grid search for the diagnosis of diabetes through two stages (1) applying eight feature scaling methods (StandardScaler, MaxAbsScaler, RobustScaler, PowerTransformer, QuantileTransformer, MinMaxScaler, Normalizer, and KBinsDiscretizer), and then identifying the best one according to different measurements (accuracy, F1-score, and Friedman ranking), and (2) tuning the hyperparameters via randomized grid search with 5-fold cross-validation to identify the best configuration that maximizes classification performance. These hyperparameters include criterion, max_depth, min_samples_split, and min_samples_leaf. The public diabetes datasets PIMA and IPDD are used to evaluate the proposed model. The results of this model are evaluated by accuracy, precision, recall, F1_score, Area Under the Curve (AUC), and Matthews Correlation Coefficient (MCC).
Results:
The results are 99.5%, 99.52%, 99.5%, 99.51%, 99.8%, and 97.97% on the IPDD dataset and 77.27%, 77.19%, 77.27%, 77.23%, 77.9%, and 70.2% on the PIMA dataset for the six metrics, respectively. Then, these results are compared against different machine learning classifiers, including classic decision tree, K-nearest neighbors, logistic regression, support vector machine, AdaBoost, random forest, gradient boosting, and XGBoost. Furthermore, the p-value is computed to analyze the feature interactions for the clinical relevance.
Conclusions:
This paper offers a scalable, interpretable, and ethically aligned tool towards improving diabetes diagnosis techniques using machine learning, contributing to early detection of the disease and reduction of associated health risks.
