A Comparative Study with Different Machine Learning Algorithms for Diabetes Disease Prediction

Kibria, Hafsa Binte; Matin, Abdul; Jahan, Nusrat; Islam, Sanzida

doi:10.1109/cce53527.2021.9633043

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…Different types of ML algorithms have been used in [ 6 , 12 , 13 , 14 , 15 , 16 , 17 ] for the diagnosis of diabetes. Since the PIDD has imbalanced classes, the preprocessing classes need to be balanced.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A vast difference was observed between sensitivity and specificity because of the imbalanced class. Kibria et al [ 6 ] found an accuracy of 83% in diabetes detection using the logistic regression (LR) where the KNN algorithm was employed for the imputation of the missing values. By using the appropriate process to replace the missing values and balance the data distribution, opportunities can be created to improve the prediction performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The contribution level of each feature behind the prediction of a decision was not explained, hence making it difficult to understand how a decision was made by the model. Most of the works [ 6 , 10 , 12 , 16 , 19 , 22 ] used the train test split for diagnosing the diabetes disease, which resulted in a high accuracy. For example in [ 19 ], the accuracy was 98% for an imbalanced class of dataset where training, validation, and testing the amount of data was 70%, 15%, and 15% respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Diabetes is the reason for the change in glucose levels in the body. Some preventive measures, such as a balanced diet and a healthy lifestyle, can be considered [ 6 ] to reduce the risks of diabetes. Diagnosing diabetes is easier with a regular medical checkup.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Kibria

Nahiduzzaman

Goni

et al. 2022

Sensors

Self Cite

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Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Kibria

Nahiduzzaman

Goni

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Kalenkoski and Lacombe (2013) [Kalenkoski and Lacombe, 2013] show that the analysis of changes in the minimum wage, for example, Card and Krueger (1994) [Card and Krueger, 2000] , might suffer from bias. Kibria, Matin, Jahan, and Islam (2021) [Kibria et al, 2021] applied machine language to predict the rapid rise in diabetes using a model to diagnose diabetes efficiently. They applied logistic regression, SVM, and k-nearest neighbor (knn) algorithms to classify diseases and predict them efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%