Diabetes Prediction Using Machine Learning: A Comparative Study

Rady, Mohamed; Moussa, Kareem; Mostafa, Mahmoud; Elbasry, Abdelrahman; Ezzat, Zeyad; Medhat, Walaa

doi:10.1109/niles53778.2021.9600091

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…This dataset comprises multiple attributes related to DM from 200 patients. Furthermore, a dataset comprising symptoms of DM, obtainable without medical examination, consisting of 521 records from Kaggle, has been employed in research [6].…”

Section: A Datasets Usedmentioning

confidence: 99%

Predicting Diabetes Mellitus with Machine Learning Techniques

Tong,

Ng,

Arul Ananthan

2024

JETAP

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This study addresses the challenge of accurately identifying diabetes mellitus in individuals. Utilizing accessible online and real-world diagnostic data, we employ machine learning models, including Support Vector Machine, Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Deep Neural Network, on the PIMA Indian Diabetes and NHANES 1999-2016 datasets. Rigorous data pre-processing steps were conducted, handling null values, outliers, and imbalanced data together with data normalization. Our results reveal that the RF model achieves a 79% accuracy for binary classification on the PIMA Indian Diabetes dataset, using a 60:40 train-test split with BORUTA selected features. Meanwhile, the XGBoost model excels on the NHANES 1999-2016 dataset, achieving 92% accuracy for binary and 91% for multiclass classification respectively.

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Section: A Datasets Usedmentioning

confidence: 99%

Predicting Diabetes Mellitus with Machine Learning Techniques

Tong,

Ng,

Arul Ananthan

2024

JETAP

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“…On a dataset of 521 instances (80% and 20% for training testing respectively), [8] authors applied 8 ML algorithms such as logistic regression, support vector machines-linear, and nonlinear kernel, random forest, decision tree, adaptive boosting classifier, K-nearest neighbor, and naïve bayes. According to the results, the Random Forest classifier achieved 98% accuracy compared to the other.…”

Section: Related Workmentioning

confidence: 99%

Diabetes Prediction Using Machine Learning Algorithms and Ontology

Massari

Sabouri

Mhammedi

et al. 2022

JICTS

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Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.

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