Hybrid Prediction Model for Type-2 Diabetes Mellitus using Machine Learning Approach

Bhat, Salliah Shafi; Selvam, Venkatesan; Ansari, Gufran Ahmad; Ansari, Mohd Dilshad

doi:10.1109/pdgc56933.2022.10053092

Cited by 7 publications

(1 citation statement)

References 20 publications

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“…The evaluation metrics employed include accuracy, precision, recall, and F1-measure, which provide comprehensive insights into the model’s performance. The accuracy is the total number of correct predicted instances over the total instances [ 50 ]. Precision is the value of true positives over the sum of true and false positives.…”

Section: Resultsmentioning

confidence: 99%

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Idris,

Ismail,

Jaya

et al. 2024

PLoS ONE

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Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

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Section: Resultsmentioning

confidence: 99%

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Idris,

Ismail,

Jaya

et al. 2024

PLoS ONE

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Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

Bhat,

Ansari,

Ansari

2024

Multimed Tools Appl

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Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

Elhoushy,

Zalam,

Sayed

et al. 2023

Biomed. Eng. Lett.

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Regulating blood glucose level (BGL) for type-1 diabetic patient (T1DP) accurately is very important issue, an uncontrolled BGL outside the standard safe range between 70 and 180 mg/dl results in dire consequences for health and can significantly increase the chance of death. So the purpose of this study is to design an optimized controller that infuses appropriate amounts of exogenous insulin into the blood stream of T1DP proportional to the amount of obtained glucose from food. The nonlinear extended Bergman minimal model is used to present glucose-insulin physiological system, an interval type-2 fuzzy logic controller (IT2FLC) is utilized to infuse the proper amount of exogenous insulin. Superiority of IT2FLC in minimizing the effect of uncertainties in the system depends primarily on the best choice of footprint of uncertainty (FOU) of IT2FLC. So a comparison includes four different optimization methods for tuning FOU including hybrid grey wolf optimizer-cuckoo search (GWOCS) and fuzzy logic controller (FLC) method is constructed to select the best controller approach. The effectiveness of the proposed controller was evaluated under six different scenarios of T1DP using Matlab/Simulink platform. A 24-h scenario close to real for 100 virtual T1DPs subjected to parametric uncertainty, uncertain meal disturbance and random initial condition showed that IT2FLC accurately regulate BGL for all T1DPs within the standard safe range. The results indicated that IT2FLC using GWOCS can prevent side effect of treatment with blood-sugar-lowering medication. Also stability analysis for the system indicated that the system operates within the stability region of nonlinear system.

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Hybrid Prediction Model for Type-2 Diabetes Mellitus using Machine Learning Approach

Cited by 7 publications

References 20 publications

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

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