Developing A Predictive Model for Diabetes Using Data Mining Techniques

Zeki, Ahmed M.; Taha, Ranyah; Alshakrani, Sara

doi:10.1109/3ict53449.2021.9582114

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) 2021

DOI: 10.1109/3ict53449.2021.9582114

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Developing A Predictive Model for Diabetes Using Data Mining Techniques

Ahmed M. Zeki

Ranyah Taha

Sara Alshakrani

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2022

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Cited by 2 publications

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“…In [12], a method for diabetes early diagnosis was proposed, several machine learning methods were utilized for classification: Naive Bayes (NB), logistic regression (LR), and random forest (RF). The model experimented on PIDD dataset and highest accuracy is achieved by RF in comparison to the other DM method utilized.…”

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confidence: 99%

Diabetes Prediction Using Binary Grey Wolf Optimization and Decision Tree

Al.Hak¹

2022

IJC

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Type 2 diabetes is a well-known lifelong condition disease that reduces the human body’s ability to produce insulin. This causes high blood sugar levels, which leads to different complications, including stroke, eye, cardiovascular, kidney, and nerve damage. Although diabetes has attained the attention of huge research, the classification performance of such medical problems utilizing techniques of machine learning is quite low, primarily due to the class imbalance and the presence of missing values in data. In this work, we proposed a model using binary Grey wolf optimization (GWO) and a Decision tree. The proposed model is composed of preprocessing, feature selection, and classification. In preprocessing, that is responsible for minority class oversampling and handling missing values. In the second step, binary GWO are used to select the most significant features. In the third step, the proposed model is trained using the Decision tree algorithm. The model achieved an accuracy of 83.11% when it was applied on the Pima Indian`s dataset.

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confidence: 99%

Diabetes Prediction Using Binary Grey Wolf Optimization and Decision Tree

Al.Hak¹

2022

IJC

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Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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Diabetes is currently one of the most common, dangerous, and costly diseases globally caused by increased blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people’s health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people’s health without compromising their comfort is an essential need. In this study, the ensemble training methodology based on genetic algorithms was used to diagnose and predict the outcomes of diabetes mellitus accurately. This study uses the experimental data, actual data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.

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Developing A Predictive Model for Diabetes Using Data Mining Techniques

Cited by 2 publications

References 12 publications

Diabetes Prediction Using Binary Grey Wolf Optimization and Decision Tree

Diabetes Prediction Using Binary Grey Wolf Optimization and Decision Tree

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

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