Predicting the Onset of Diabetes with Machine Learning Methods

Chou, Chun-Yang; Hsu, Ding‐Yang; Chou, Chun-Hung

doi:10.3390/jpm13030406

Cited by 55 publications

(23 citation statements)

References 39 publications

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“…In another study, Syed and Khan [69] developed a data-driven predictive model to screen for T2DM in the western region of Saudi Arabia, achieving 82.1% accuracy, 77.6% precision, 89% recall, 0.867 ROC-AUC, and 82.9% F1-score. Next, Chou et al [70] predicted the onset of diabetes using ML methods, achieving 95.3% accuracy, 92.7% precision, 93.1% recall, 0.991 ROC-AUC, and 92.9% F1-score. Then, Laila et al [71] used an ensemble-based ML model to predict diabetes with an accuracy of 97.11%, precision of 97.1%, recall of 97.1%, and an F1-score of 97.1%.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

et al. 2023

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Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

et al. 2023

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“…Subsequently, a range of machine learning techniques, such as logistic regression, decision trees, and support vector machines, will be applied to the preprocessed data to develop research or predictive models. The performance of these models will be evaluated using appropriate evaluation metrics, such as accuracy, sensitivity, speci city, and area under the receiver operating characteristic curve [25] by following this methodology, valuable insights can be gained regarding the early detection and prediction of diabetes, potentially leading to timely inter venations and improved healthcare outcomes.…”

Section: Deepti Sisodia and Dilipmentioning

confidence: 99%

Early Stage Diabetes Prediction by Approach Using Machine Learning Techniques

Zarar

Wang

2023

Preprint

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Diabetes is the most viral and chronic disease throughout the world. A large number of people are affected by this chronic disease. Early detection of diabetes in a patient is crucial for ensuring a good quality of life. Machine learning techniques or Data Mining Techniques are playing a significant role in today’s life to detect diabetes and improve performance to make further accurate predictions. The aim of this research is diabetes prediction with the approach of machine learning techniques. In this technical approach, we have taken two data sets Pi-ma Indian diabetes data set and the Kaggle diabetes data set, and proposed a model for diabetes prediction. We have used four different machine learning algorithms such as Support Vector Machine, Decision Forest, Linear Regression, and Artificial Neural Network. In these machine learning algorithms, ANN gives the best prediction performance where the highest accuracy is 98.8% so, it could be used as an alternative method to support predict diabetes complication diseases at an initial stage. Further, this work can be extended to find how likely non-diabetic people can have diabetes in the next few years and also, this predicted model can be used for imaging processing in the future to find diabetes for the prediction of diabetic and non-diabetic.

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“…The recall is a metric that evaluates the ability of a model to identify all the relevant cases within a dataset [14]. Mathematically, recall can be computed using the formula below:…”

Section: Recallmentioning

confidence: 99%

Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed

Shaukat,

Zafar,

Ahmad

et al. 2023

Healthcare

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The intricate and multifaceted nature of diabetes disrupts the body’s crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew’s correlation coefficient, receiver operating characteristic area, and precision–recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain.

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Predicting the Onset of Diabetes with Machine Learning Methods

Cited by 55 publications

References 39 publications

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

Early Stage Diabetes Prediction by Approach Using Machine Learning Techniques

Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed

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