A novel hybrid machine learning framework for the prediction of diabetes with context-customized regularization and prediction procedures

Rajagopal, Aghila; Jha, Sudan; Alagarsamy, Ramachandran; Quek, Shio Gai; Selvachandran, Ganeshsree

doi:10.1016/j.matcom.2022.03.003

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…The result of the anticipated model was 85.83%. Rajagopal et al [20] proposed a model to predict diabetes. In this research, the researcher used "Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019".…”

Section: Review Of Literaturementioning

confidence: 99%

A genetic algorithm-based feature selection approach for diabetes prediction

Kangra,

Singh

2024

IJ-AI

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<p>Genetic algorithms have emerged as a powerful optimization technique for feature selection due to their ability to search through a vast feature space efficiently. This study discusses the importance of feature selection for prediction in healthcare and prominently focuses on diabetes mellitus. Feature selection is essential for improving the performance of prediction models, by finding significant features and removing unnecessary among them. The study aims to identify the most informative subset of features. Diabetes is a chronic metabolic disorder that poses significant health challenges worldwide. For the experiment, two datasets related to diabetes were downloaded from Kaggle and the results of both (datasets) with and without feature selection using the genetic algorithm were compared. Machine learning classifiers and genetic algorithms were combined to increase the precision of diabetes risk prediction. In the preprocessing phase, feature selection, machine learning classifiers, and performance metrics methods were applied to make this study feasible. The results of the experiment showed that genetic algorithm + logistic regression i.e., 80% (accuracy) works better for PIMA diabetes, and for Germany diabetes dataset genetic algorithm + random forest and genetic algorithm + K-Nearest Neighbor i.e., 98.5% performed better than other chosen classifiers. The researchers can better comprehend the importance of feature selection in healthcare through this study.</p>

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Section: Review Of Literaturementioning

confidence: 99%

A genetic algorithm-based feature selection approach for diabetes prediction

Kangra,

Singh

2024

IJ-AI

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“…In the study of chronic disease prediction, most of the data had outliers removed. Removing outliers was done with IQR techniques [62], [102]-- [104]. Yuk Hyeonseop, et al [102] considered outliers, stipulating that any data point with a pulse rate variable greater than or equal to 300 should be excluded from the analysis.…”

Section: ) Outliermentioning

confidence: 99%

“…In predicting chronic disease, the GA algorithm is mostly hybrid with other methods such as ANN with a result accuracy of 80% [104]. The study focused solely on measuring accuracy results.…”

Section: H Genetic Algorithmmentioning

confidence: 99%

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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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“…In United States almost one in every 10 individuals is diabetic. Diabetes research is therefore essential, including studies of diabetes prediction and its effects on health [3].…”

Section: Introductionmentioning

confidence: 99%

Improving the Diabetes Diagnosis Prediction Rate Using Data Preprocessing, Data Augmentation and Recursive Feature Elimination Method

Sabitha¹,

Durgadevi²

2022

IJACSA

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Hyperglycemia is a symptom of diabetes mellitus, a metabolic condition brought on by the body's inability to produce enough insulin and respond to it. Diabetes can damage body organs if it is not adequately managed or detected in a timely manner. Many years of research into diabetes diagnosis has led to a suitable method for diabetes prediction. However, there is still scope for improvement regarding precision. The paper's primary objective is to emphasize the value of data preprocessing, feature selection, and data augmentation in disease prediction. Techniques for data preprocessing, feature selection, and data augmentation can assist classification algorithms function more effectively in the diagnosis and prediction of diabetes. A proposed method is employed for diabetes diagnosis and prediction using the PIMA Indian dataset. A systematic framework for conducting a comparison analysis based on the effectiveness of a three-category categorization model is provided in this study. The first category compares the model's performance with and without data preprocessing. The second category compares the performance of five alternative algorithms employing the Recursive Feature Elimination (RFE) feature selection method. Data augmentation is the third category; data augmentation is done with SMOTE Oversampling, and comparisons are made with and without SMOTE Oversampling. On the PIMA Indian Diabetes dataset, studies showed that data preprocessing, RFE with Random Forest Regression feature selection, and SMOTE Oversampling augmentation can produce accuracy scores of 81.25% with RF, 81.16 with DT, and 82.5% with SVC. From Six Classifiers LR, RF, DT, SVC, GNB and KNN, it is observed that RF, DT, and SVC performed better in accuracy level. The comparative study enables us to comprehend the value of data preprocessing, feature selection, and data augmentation in the disease prediction process as well as how they affect performance.

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A novel hybrid machine learning framework for the prediction of diabetes with context-customized regularization and prediction procedures

Cited by 10 publications

References 28 publications

A genetic algorithm-based feature selection approach for diabetes prediction

A genetic algorithm-based feature selection approach for diabetes prediction

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Improving the Diabetes Diagnosis Prediction Rate Using Data Preprocessing, Data Augmentation and Recursive Feature Elimination Method

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