Diagnosis of Diabetes using Support Vector Machines with Radial Basis Function Kernels

Abdillah, Abdul Azis; Suwarno, Suwarno

doi:10.14716/ijtech.v7i5.1370

Cited by 20 publications

(6 citation statements)

References 8 publications

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“…The development of artificial intelligence in the medical field is very rapid. Some of them are diabetes detection [1][2][3], brain detection [4][5][6], cancer [7,8], heart disease [9][10][11] and others. Disease detection using artificial intelligence has also been used by the medical team to be an early diagnosis in detecting an abnormal condition.…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

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: Linear Discriminant Analysismentioning

confidence: 99%

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

Abdillah

Azwardi

Permana

et al. 2021

EEJET

Self Cite

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“…Abdul AzisAbdullah [11] et al mentioned the Diagnosis of Diabetes using Support Vector Machines with Radial Basis Function Kernels.…”

Section: Related Workmentioning

confidence: 99%

“…Root nodes can have two or more branches while the leaf nodes represent classification. In every stage, Decision tree chooses each node by evaluating the highest information gain among all the attributes [11]. The Fig.6…”

Section: Fig 2: Matrix Of Scatterplot In Logistic Regressionmentioning

confidence: 99%

Implementing Classification Algorithms for Predicting Chronic Diabetes Diseases

Kavitha¹,

Subbaiah²

2019

IJEAT

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Now a day Chronic Diabetes Disease is increasing due to many reasons like changes in life style, food habit. It causes an increase in blood sugar levels. If Diabetes Disease remains untreated or unidentified, many different types of complications may be occurred. The doctors have the problem to identify these kinds of diseases easily. The machine learning algorithms helps the doctor to solve these types of problems. In this paper, we implemented three algorithms namely logistic regression, Naive Bayes and Decision tree algorithms to predict diabetes at an early stage. Experiments are performed on Pima Indians Diabetes Dataset, which is from UCI machine learning repository. The performance of all the three algorithms is evaluated using measures on Accuracy. Results obtained showed logistic regression displays 75.3%, Decision tree displays 77.9% and Naive Bayes classifier displays the accuracy value is 76.6%.

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“…The majority of medical diagnoses are based on a binary decision (for example the patient is diabetic or non-diabetic), hence the interest of classification into two categories. The classifiers generally used in the classification phase are Support Vector Machine (SVM) (Abdillah & Suwarno, 2016), Gaussian Process Classification (GPC) (Maniruzzaman et al, 2017), Random Forest (RF) (Nai-arun & Moungmai, 2015;Zou el al., 2018), and Convolutional Neural Network (CNN) .…”

mentioning

confidence: 99%

“…For this reason, ensemble learning (combination of classifiers) has become the widely adopted tendency in medical diagnostic support systems. Currently, EL occupies an important place in the field of Machine Learning (ML) and Artificial Intelligence (AI) because of these honorable results in various applications (Lamari et al, 2021;Abdillah & Suwarno 2016;Touahri et al, 2021). The main idea of EL contributes to improving the performance of unbalanced learning by merging various classifiers so as to reduce variance, bias, and/or otherwise enhance predictions.…”

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confidence: 99%

Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier

Zemmal

Benzebouchi

Azizi

et al. 2022

International Journal of Intelligent Information Technologies

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Diabetes is characterized by an abnormally enhanced concentration of glucose in the blood serum. It has a damaging impact on several noble body systems. Today, the concept of unbalanced learning has developed considerably in the domain of medical diagnosis, which greatly reduces the generation of erroneous classification results. The paper takes a hybrid approach to imbalanced learning by proposing an enhanced multimodal meta-learning method called IRESAMPLE+St to distinguish between normal and diabetic patients. This approach relies on the Stacking paradigm by utilizing the complementarity that may exist between classifiers. In the same focus of this study, a modified RESAMPLE-based technique referred to as IRESAMPLE+ and the SMOTE method are integrated as a preliminary resampling step to overcome and resolve the problem of unbalanced data. The suggested IRESAMPLE+St provides a computerized diabetes diagnostic system with impressive results, comparing it to the principal related studies, reflecting the design and engineering successes achieved.

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Diagnosis of Diabetes using Support Vector Machines with Radial Basis Function Kernels

Cited by 20 publications

References 8 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

Implementing Classification Algorithms for Predicting Chronic Diabetes Diseases

Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier

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