Performance analysis and prediction of type 2 diabetes mellitus based on lifestyle data using machine learning approaches

Ganie, Shahid Mohammad; Malik, Majid Bashir; Arif, Tasleem

doi:10.1007/s40200-022-00981-w

Cited by 21 publications

(9 citation statements)

References 32 publications

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“…Machine/ensemble learning techniques, with their potential to deliver consistent, reliable, and valid results, are used in almost every sphere of life to solve real-life problems [4,5]. Copious work has been done for disease prediction using ML and EL techniques [6]. Researchers have explored different datasets, algorithms, and methodologies to conduct future research in diagnosing cardiovascular disease [7,8].…”

Section: Related Workmentioning

confidence: 99%

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An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Ganie¹,

Pramanik²,

Malik³

et al. 2023

Computer Systems Science and Engineering

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Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method. Results were recorded before and after the data preprocessing techniques were applied. Out of all the algorithms, gradient boosting achieved the highest accuracy rate of 92.20% for the proposed model. The proposed model yielded better results with gradient boosting in terms of precision, recall, and f1-score. It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

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Section: Related Workmentioning

confidence: 99%

“…These models have made significant progress in better prediction, detection, diagnosis, and prognosis of different diseases. In this study, for heart disease prediction, we considered the following three ensemble-learning-based boosting algorithms [6]:…”

Section: Techniques Usedmentioning

confidence: 99%

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Ganie¹,

Pramanik²,

Malik³

et al. 2023

Computer Systems Science and Engineering

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“…Furthermore, based on current evidence, a substantial association between lifestyle and diabetes has been demonstrated [ 18 , 19 ]. However, only two studies [ 20 , 21 ] included lifestyle variables to train machine learning. The first study [ 21 ] omitted the more critical factors of diet and physical activity.…”

Section: Introductionmentioning

confidence: 99%

“…However, only two studies [ 20 , 21 ] included lifestyle variables to train machine learning. The first study [ 21 ] omitted the more critical factors of diet and physical activity. The second study [ 20 ] predicted cognitive health in adults but did not predict diabetes.…”

Section: Introductionmentioning

confidence: 99%

“…The second study [ 20 ] predicted cognitive health in adults but did not predict diabetes. In addition, it [ 21 ] does not apply the CATBoost and XGBoost machine-learning models that have emerged in recent years, which may explain its machine-learning model’s poor AUC (area under the curveroc). Therefore, our study included significant predictors, a larger number of training people, and CATBoost, XGBoost, and other models that have emerged in recent years to optimize the prediction of diabetes using lifestyle variables.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type

Qin

et al. 2022

IJERPH

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The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning models for diabetes prediction using lifestyle data from the National Health and Nutrition Examination Survey (NHANES) database. The 1999–2020 NHANES database yielded data on 17,833 individuals data based on demographic characteristics and lifestyle-related variables. To screen training data for machine models, the Akaike Information Criterion (AIC) forward propagation algorithm was utilized. For predicting diabetes, five machine-learning models (CATBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM)) were developed. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic (ROC) curve. Among the five machine-learning models, the dietary intake levels of energy, carbohydrate, and fat, contributed the most to the prediction of diabetes patients. In terms of model performance, CATBoost ranks higher than RF, LG, XGBoost, and SVM. The best-performing machine-learning model among the five is CATBoost, which achieves an accuracy of 82.1% and an AUC of 0.83. Machine-learning models based on NHANES data can assist medical institutions in identifying diabetes patients.

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Performance Evaluation of Various Machine Learning Algorithms for Lung Cancer Prediction Using Demographic Data

Sharmila,

Kumar,

Ganie

et al. 2024

Communications in Computer and Information Science

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Performance analysis and prediction of type 2 diabetes mellitus based on lifestyle data using machine learning approaches

Cited by 21 publications

References 32 publications

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type

Performance Evaluation of Various Machine Learning Algorithms for Lung Cancer Prediction Using Demographic Data

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