Classification and regression tree model for diabetes prediction Article Swipe

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Farah Najidah Noorizan , Nur Anida Jumadi , Li Mun Ng , Farah Najidah Noorizan , Nur Anida Jumadi , Li Mun Ng ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.11591/ijict.v15i1.pp207-216

Diabetes mellitus is characterized by excessive blood glucose that occurs when the pancreas malfunctions while producing insulin. High blood glucose levels can cause chronic damage to organs, particularly the eyes and kidneys. Diabetes prediction models traditionally use a variety of machine learning (ML) algorithms by combining data from the glucose levels, patient health parameters, and other biomarkers. Prior research on diabetes prediction using various algorithms, such as support vector machine (SVM) and decision tree (DT) models, demonstrates an accuracy rate of approximately 70%, which is relatively modest. Therefore, in this study, a classification and regression tree (CART) multiclassifier model has been proposed to improve the accuracy of diabetes prediction, which is based on three classes: non-diabetic, pre-diabetic, and diabetic. The study involved data preprocessing steps, hyperparameter tuning, and evaluation of performance metrics. The model achieved 97% accuracy while utilizing the value of 5 for the number of leaves per node, the value of 10 for the maximum number of splits, and deviance as the split criterion, which also resulted in a precision of 98%, recall of 97%, and F1-score of 98%, showing that the proposed multiclassifier model can accurately predict diabetes. In conclusion, the proposed CART model with the best hyperparameter setting can enable the highest accuracy in predicting diabetes classes.

Related Topics

Decision Tree

Artificial Intelligence

Machine Learning

Support Vector Machine

Decision Tree Learning

Concepts

Hyperparameter Decision tree Artificial intelligence Machine learning Regression Support vector machine Diabetes mellitus Computer science Decision tree model Regression analysis Random forest Statistics Decision tree learning Predictive modelling Data pre-processing Preprocessor Tree (set theory) Cart Precision and recall Pattern recognition (psychology) Data mining Mathematics Linear regression Medicine

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Landing Page: https://doi.org/10.11591/ijict.v15i1.pp207-216
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Title: Classification and regression tree model for diabetes prediction

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Publication year: 2025

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Publication date: 2025-12-12

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Authors: Farah Najidah Noorizan, Nur Anida Jumadi, Li Mun Ng, Farah Najidah Noorizan, Nur Anida Jumadi, Li Mun Ng

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Concepts: Hyperparameter, Decision tree, Artificial intelligence, Machine learning, Regression, Support vector machine, Diabetes mellitus, Computer science, Decision tree model, Regression analysis, Random forest, Statistics, Decision tree learning, Predictive modelling, Data pre-processing, Preprocessor, Tree (set theory), Cart, Precision and recall, Pattern recognition (psychology), Data mining, Mathematics, Linear regression, Medicine

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abstract_inverted_index.occurs	9
abstract_inverted_index.recall	174
abstract_inverted_index.steps,	124
abstract_inverted_index.study,	90
abstract_inverted_index.vector	68
abstract_inverted_index.chronic	23
abstract_inverted_index.glucose	7, 19, 49
abstract_inverted_index.highest	205
abstract_inverted_index.improve	103
abstract_inverted_index.levels,	50
abstract_inverted_index.machine	40, 69
abstract_inverted_index.maximum	156
abstract_inverted_index.models,	75
abstract_inverted_index.modest.	86
abstract_inverted_index.organs,	26
abstract_inverted_index.patient	51
abstract_inverted_index.predict	189
abstract_inverted_index.setting	201
abstract_inverted_index.showing	181
abstract_inverted_index.splits,	159
abstract_inverted_index.support	67
abstract_inverted_index.tuning,	126
abstract_inverted_index.variety	38
abstract_inverted_index.various	63
abstract_inverted_index.Diabetes	0, 32
abstract_inverted_index.F1-score	178
abstract_inverted_index.accuracy	78, 105, 136, 206
abstract_inverted_index.achieved	134
abstract_inverted_index.classes.	210
abstract_inverted_index.classes:	114
abstract_inverted_index.decision	72
abstract_inverted_index.deviance	161
abstract_inverted_index.diabetes	60, 107, 209
abstract_inverted_index.insulin.	16
abstract_inverted_index.involved	121
abstract_inverted_index.kidneys.	31
abstract_inverted_index.learning	41
abstract_inverted_index.mellitus	1
abstract_inverted_index.metrics.	131
abstract_inverted_index.pancreas	12
abstract_inverted_index.proposed	101, 184, 194
abstract_inverted_index.research	58
abstract_inverted_index.resulted	168
abstract_inverted_index.combining	45
abstract_inverted_index.diabetes.	190
abstract_inverted_index.diabetic.	118
abstract_inverted_index.excessive	5
abstract_inverted_index.precision	171
abstract_inverted_index.producing	15
abstract_inverted_index.utilizing	138
abstract_inverted_index.Therefore,	87
abstract_inverted_index.accurately	188
abstract_inverted_index.algorithms	43
abstract_inverted_index.criterion,	165
abstract_inverted_index.evaluation	128
abstract_inverted_index.predicting	208
abstract_inverted_index.prediction	33, 61
abstract_inverted_index.regression	94
abstract_inverted_index.relatively	85
abstract_inverted_index.algorithms,	64
abstract_inverted_index.biomarkers.	56
abstract_inverted_index.conclusion,	192
abstract_inverted_index.parameters,	53
abstract_inverted_index.performance	130
abstract_inverted_index.prediction,	108
abstract_inverted_index.demonstrates	76
abstract_inverted_index.malfunctions	13
abstract_inverted_index.particularly	27
abstract_inverted_index.approximately	81
abstract_inverted_index.characterized	3
abstract_inverted_index.non-diabetic,	115
abstract_inverted_index.pre-diabetic,	116
abstract_inverted_index.preprocessing	123
abstract_inverted_index.traditionally	35
abstract_inverted_index.classification	92
abstract_inverted_index.hyperparameter	125, 200
abstract_inverted_index.multiclassifier	97, 185
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	6
citation_normalized_percentile.value	0.88667451
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True