Classification Of Diabetes Patients Using Kernel Based Support Vector Machines

Pethunachiyar, G. A.

doi:10.1109/iccci48352.2020.9104185

Cited by 35 publications

(12 citation statements)

References 7 publications

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“…But the suggested approach is not compared to current methods. Automated categorization of diabetes using a machine learning technique was given by ( 13 ). They employed a SVM classifier using hyperglycemia samples from the UCI Machine Archive.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

Rabie

Alghazzawi

Asghar

et al. 2022

Front. Public Health

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Background and ObjectiveAccording to the WHO, diabetes mellitus is a long-term condition marked by high blood sugar levels. The consequences might be far-reaching. According to current increases in mortality, diabetes has risen to number 10 among the leading causes of mortality worldwide. When used to predict diabetes using unbalanced datasets from testing, machine learning (ML) classifiers and established approaches for encoding categorical data have exhibited a broad variety of surprising outcomes. Early studies also made use of an artificial neural network to extract features without obtaining a grasp of the sequence information.MethodsThis study offers a deep learning-based decision support system (DSS), utilizing bidirectional long/short-term memory (BiLSTM), to accurately predict diabetic illness from patient data. In order to predict diabetes, the BiLSTM hybrid model was used after balancing the data set.ResultsUnlike earlier studies, this proposed model's trial findings were promising, with an accuracy of 93.07%, 93% precision, 92% recall, and a 92% F1-score.ConclusionsUsing a BILSTM model for classification outperforms current approaches in the diabetes detection domain.

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Section: Related Workmentioning

confidence: 99%

A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

Rabie

Alghazzawi

Asghar

et al. 2022

Front. Public Health

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“…Remarkably, we tweaked MLP for grouping because of its promising presentation in medical services, explicitly in diabetes forecast [68][69][70][71]. The creators [72] proposed a strategic relapse model in light of photoplethysmogram examination for diabetes detection. The proposed framework accurately calculated 92% as nondiabetic.…”

Section: Diabeticsmentioning

confidence: 99%

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Siddegowda

Devi

2022

IJMTS

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Background/ Purpose: Reviewing of the various work and literature in the proposed areas will help in developing a strong foundation of the domain on which the research is planned. The reason forth for the literature review is to become familiar in the health care domain. Since the area selected is the health care domain, the recent literature review is carried out as it is very important. Objective: A strong background on health care domain is developed and a new problem which is not addressed is discussed. The gaps in the research area are identified. A new solution for solving the problem is designed and developed. Design/Methodology: This work has adapted secondary source of data which is mainly journals, articles and review comments. The relevant literature is selected and a detailed study is conducted. This has helped in drafting the problem statement. Findings/Results: The finding and drawbacks of all the recent work are well studied. The reason for the gap is also well studied and the results of each work are also well analyzed. Research Limitations: A detailed study done on the chronic diseases and its impact has helped to open up the importance of studying about comorbid diseases. The limitations of various machine learning algorithms are also studied. Originality/Value: This paper aims at studying the relevant existing literature that includes research journals, conference papers, technical book chapter and few web sources. All the papers selected were relevant to the proposed work and all papers are recent and from well reputed publisher. The papers are cited by many authors. Paper Type: Literature review paper is carried out on scientific papers, especially from well indexed services.

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“…However, the proposed technique is not compared with state-of-the-art techniques. Pethunachiyar [16] presented a diabetes mellitus classification system using a machine learning algorithm. Mainly, he used a support vector machine with different kernel functions and diabetes data from the UCI Machine Repository.…”

Section: Diabetes Classification For Healthcare Health Conditionmentioning

confidence: 99%

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Butt

Letchmunan

Ali

et al. 2021

Journal of Healthcare Engineering

102

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The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

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Classification Of Diabetes Patients Using Kernel Based Support Vector Machines

Cited by 35 publications

References 7 publications

A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

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