Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed

Shaukat, Zain; Zafar, Wisal; Ahmad, Waqas; Haq, Ihtisham Ul; Husnain, Ghassan; Al-Adhaileh, Mosleh Hmoud; Ghadi, Yazeed Yasin; Algarni, Abdulmohsen

doi:10.3390/healthcare11212864

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…The study discusses various data sources, including electronic health records and wearable devices, and their utilization in training predictive models. Additionally, it delves into challenges such as data imbalance and feature selection in diabetes prediction, providing a well-rounded understanding machine learning for diabetes management [8].…”

Section: ░ 2 Literature Surveymentioning

confidence: 99%

A Survey on Implementing Machine Learning Algorithm to Predict Diabetes Stages and Preventing Elevated Blood Glucose Levels

Kathiravan A.,

kumar,

Kanimozhi

2023

IIJSR

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Diabetes, a chronic metabolic disorder, A health condition with rising prevalence. Accurate prediction of diabetes stages and proactive management of elevated blood glucose levels are crucial for effective treatment and prevention of complications. This study investigates the implementation of machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbors (KNN), to address these critical aspects of diabetes care. In this research, a comprehensive data-set comprising clinical and demographic information of individuals was utilized. Data preprocessing techniques, including feature selection, normalization, and handling of missing values, were employed to prepare the data-set for modeling. These Algorithms were trained and evaluated for their effectiveness in predicting diabetes stages and identifying individuals at risk of elevated blood glucose levels. The previous techniques used in diabetes prediction performs with moderate accuracy and optimization which will not sufficient to attain maximum level of prediction.

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Section: ░ 2 Literature Surveymentioning

confidence: 99%

A Survey on Implementing Machine Learning Algorithm to Predict Diabetes Stages and Preventing Elevated Blood Glucose Levels

Kathiravan A.,

kumar,

Kanimozhi

2023

IIJSR

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Artificial Intelligence in Diabetes Management: Revolutionizing the Diagnosis of Diabetes Mellitus; a Literature Review

Keshtkar,

Ayareh,

Atighi

et al. 2024

Shiraz E-Med J

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Context: The diagnostic methods for diabetes mellitus (DM), a chronic metabolic disorder characterized by elevated blood sugar levels, are rapidly evolving thanks to artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL). This review explores the applications of AI in risk assessment and diagnosing different types of diabetes. Evidence Acquisition: The review highlights the effectiveness of various ML models, including support vector machines (SVMs), random forests (RFs), and DL techniques like convolutional neural networks (CNNs), in achieving high diagnostic accuracy. Challenges include limited data availability, interpretability of complex models, and the need for standardized performance metrics. Results: Machine learning methods like SVMs and RFs are highly effective at diagnosing different types of diabetes, and DL techniques like CNNs also show great promise. Conclusions: Overall, AI has immense potential to revolutionize diabetes diagnosis by facilitating risk assessment and early detection, improving treatment efficacy, and preventing severe complications.

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Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population

Shi,

Sun

2024

PeerJ Computer Science

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As the population ages, the increase in the number of middle-aged and older adults with diabetes poses new challenges to the allocation of resources in the healthcare system. Developing accurate diabetes prediction models is a critical public health strategy to improve the efficient use of healthcare resources and ensure timely and effective treatment. In order to improve the identification of diabetes in middle-aged and older patients, a Bagging-RF model is proposed. In the study, two diabetes datasets on Kaggle were first preprocessed, including unique heat coding, outlier removal, and age screening, after which the data were categorized into three age groups, 50–60, 60–70, and 70–80, and balanced using the SMOTE technique. Then, the machine learning classifiers were trained using the Bagging-RF integrated model with eight other machine learning classifiers. Finally, the model’s performance was evaluated by accuracy, F1 score, and other metrics. The results showed that the Bagging-RF model outperformed the other eight machine learning classifiers, exhibiting 97.35%, 95.55%, 95.14% accuracy and 97.35%, 97.35%, 95.14% F1 Score at the Diabetes Prediction Dataset for diabetes prediction for the three age groups of 50–60, 60–70, and 70–80; and 97.03%, 94.90%, 93.70% accuracy and 97.03%, 94.90%, 93.70% F1 Score at the Diabetes Prediction Dataset. 95.55%, 95.13% F1 Score; and 97.03%, 94.90%, 93.70% accuracy; and 97.03%, 94.89%, 93.70% F1 Score at Diabetes Prediction Dataset. In addition, while other integrated learning models, such as ET, RF, Adaboost, and XGB, fail to outperform Bagging-RF, they also show excellent performance.

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Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed

Cited by 3 publications

References 23 publications

A Survey on Implementing Machine Learning Algorithm to Predict Diabetes Stages and Preventing Elevated Blood Glucose Levels

A Survey on Implementing Machine Learning Algorithm to Predict Diabetes Stages and Preventing Elevated Blood Glucose Levels

Artificial Intelligence in Diabetes Management: Revolutionizing the Diagnosis of Diabetes Mellitus; a Literature Review

Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population

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