Early Detection of Diabetes Mellitus using Feature Selection and Fuzzy Support Vector Machine

Lukmanto, Rian Budi; Suharjito, Suharjito; Nugroho, Ariadi; Akbar, Habibullah

doi:10.1016/j.procs.2019.08.140

Cited by 47 publications

(15 citation statements)

References 21 publications

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“…While the study [16] related to cardiotocography (CTG) with 17 features, after reducing the dimensions, the accuracy obtained was more than 94 %. In addition, the same constraint was also found in the study [17] in the case of diabetes detection. Feature selection was used to identify important features in the data set.…”

Section: Literature Review and Problem Statementsupporting

confidence: 70%

Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section

Abdillah

Azwardi

Permana

et al. 2021

EEJET

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Currently the hospital is a place that is very vulnerable to the transmission of Covid-19, so giving birth in a hospital is very risky. In addition, the hospital currently only accepts cesarean deliveries, while mothers who can give birth vaginally are recommended to give birth in a midwife because the chances of being exposed to Covid-19 are much lower. In general, this study aims to examine the performance of the LDA-SVM method in predicting whether a prospective mother needs to undergo a C-section or simply give birth normally. The aims of this study are: 1) to determine the best parameters for building the detection model; 2) to determine the best accuracy from the model; 3) to compare the accuracies with the other methods. The data used in this study is the dataset of caesarian section. This data consists of the results of 80 pregnant women following C-section with the most important characteristics of labor problems in the clinical field. Based on the results of the experiments that have been carried out, several parameter values that provide the best results for building the detection model are obtained, namely σ (sigma) –5.9 for 70 % training data, σ=4, –6.1 and ‑6.6 for 80 % training data and σ=4 and 16 for 90 % training data. Besides, the results obtained show that the LDA-SVM method is able to classify the C-section method properly with an accuracy of up to 100 %. This research is also able to surpass the methods in previous studies. The results show that LDA-SVM for this case study generates an accuracy of 100.00 %. This method has great potential to be used by doctors used as an early detection to determine whether a mother needs to go through a C-section or simply give birth vaginally. So that mothers can prevent the transmission of Covid-19 in the hospital

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Section: Literature Review and Problem Statementsupporting

confidence: 70%

Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section

Abdillah

Azwardi

Permana

et al. 2021

EEJET

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“…The results demonstrated 87.7% accuracy, 92.2% sensitivity, and 80.3% specificity. Lukmanto et al [ 26 ] proposed a model for the early detection of diabetes. The feature selection technique was used to get the optimal features from the dataset.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Rule-Based System for Classification of Diabetes

Aamir

Sarfraz

Ramzan

et al. 2021

Sensors

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Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

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“…Choubey et al [25] used feature selection scheme using PSO-SVM and later fuzzy decision tree classifier is applied for diabetes classification. Lukmanto et al [18] used F-score feature selection and fuzzy support vector machine for classification. SVM is used for training the dataset which helps to generate the fuzzy rules.…”

Section: Literature Surveymentioning

confidence: 99%

“…The selection of optimal attributes is an important task. Several techniques are present based on feature selection scheme such as rough set [16], genetic algorithm [17], missing value imputation & F-score based feature selection [18]. These techniques of feature selection are used to choose substantial features which helps to improve the classification accuracy performance.…”

Section: Introductionmentioning

confidence: 99%

Logistic regression and Random forest-based hybrid classifier with recursive feature elimination technique for diabetes classification

L¹

2020

IJATCSE

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Diabetes mellitus is a chronic metabolic ailment being considered as one of the deadliest diseases in the world. Millions of cases and deaths have been reported due to diabetes. Initial forecast of diabetes condition helps in reducing the death rate significantly that happen due to it. Current advancements in biomedical techniques have facilitated to store the electronic health record datasets which can be analyzed for better diagnosis. In order to explore these datasets, data mining techniques are considered as promising techniques which examines the data rapidly and provides a desired outcome. In this work, the data mining technique was adopted for diabetes classification using machine learning techniques. The proposed approach comprises of several steps such as missing value imputation, attribute selection and classification which are performed using mean missing value imputation, logistic regression &Recursive Feature Elimination for feature selection and random forest for classification, respectively. The evaluation results demonstrate that projected methods achieve97.39% prediction accuracy which shows a significant improvement in contrast to prevailing current approaches.

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Early Detection of Diabetes Mellitus using Feature Selection and Fuzzy Support Vector Machine

Cited by 47 publications

References 21 publications

Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section

Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section

A Fuzzy Rule-Based System for Classification of Diabetes

Logistic regression and Random forest-based hybrid classifier with recursive feature elimination technique for diabetes classification

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