Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines

Parthiban, G.; Rajesh, Aashish; Srivatsa, S. K.

doi:10.5120/7324-0149

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…Their proposed system was expected to give good performance. (Parthiban, Rajesh, & Srivatsa, 2012) built a system to predict the vulnerability of diabetic patients to heart diseases using SVM. Their system exhibited good prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Support vector machine is a supervised machine learning approach used for classification problems (Kumari & Chitra, 2013). In this approach, training data is used to build classification predictive model (Parthiban, Rajesh, & Srivatsa, 2012). In this model, training vectors are represented in a high dimensional space and labelled by its class (Kumari & Chitra, 2013).…”

Section: Support Vector Machinementioning

confidence: 99%

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A Method for Classification Using Data Mining Technique for Diabetes: A Study of Health Care Information System

Al-Khasawneh

2015

International Journal of Healthcare Information Systems and Informatics

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Many researchers in the health information system field have been attracted to develop computer applications that help in the diagnosis process. Imperatively, data mining algorithms address the vital role in all of these applications. Many contributions were made in this area. There has always been a debate on the algorithm that gives the best classifier, the parameters to be used, the dataset pre-processing steps, etc. In this paper, the author largely emphasizes that the best way to build a predictive model with relatively high classification accuracy is to build several predictive models and to choose the model that gives the best results through parameters optimization. Diagnosing diabetes mellitus has gained considerable attention in the last few decades due to the increased severity of the disease. In this research, the author reviews four predictive data mining approaches that are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset; k-nearest neighbour, support vector machine, multilayer perceptron neural network, and naive bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

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Section: Related Workmentioning

confidence: 99%

Section: Support Vector Machinementioning

confidence: 99%

A Method for Classification Using Data Mining Technique for Diabetes: A Study of Health Care Information System

Al-Khasawneh

2015

International Journal of Healthcare Information Systems and Informatics

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“…There is no automated diagnosis method to diagnose Heart disease for diabetic patient based on diabetes diagnosis attributes to our knowledge. This research paper is related to our previous work, diagnosis of heart disease for diabetic patients using Naïve bayes method [23] and Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines [24] to predict the heart disease for diabetic patients using diabetic diagnosis attributes.…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients

Parthiban¹,

Srivatsa²

2012

IJAIS

Self Cite

104

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Classifying data is a common task in Machine learning. Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Heart disease is the leading cause of death in the world over the past 10 years. Researches have been using several data mining techniques in the diagnosis of heart disease. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Machine learning methods such as Naïve Bayes and Support Vector Machines for the classification purpose. Support vector machines are a modern technique in the field of machine learning and have been successfully used in different fields of application. Using diabetics' diagnosis, the system exhibited good accuracy and predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.

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“…10 fold cross validation is performed to evaluate the performance of this classifier model on training data. In 10 fold cross validation criteria, first divide the whole dataset into 10 equal size subsets and trained nine subsets and testes the rest one, finally percentage these ten correctly classified accuracy [5]. When best model generated with optimal feature subset then classifiers are applied to evaluate the performance of test set.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Data mining application in healthcare is usefulness to predict disease which can provide better decision to physicians, doctors and a cost effective treatment to patients [4]. In the knowledge discovery process, after performing data cleaning, data integration, data selection and data transformation, different data mining task such as classification, regression, clustering, association rule and summarization are used to uncover the hidden relationship of data and evaluate the valuable knowledge [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm

Hashi¹,

Zaman²,

Hasan³

2017

IJCA

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Data mining techniques are being used extensively in healthcare sector to discover hidden pattern and relationship between patients' record and their medical diagnosis dataset. In the concept of disease prediction, high classification accuracy can be obtained from accurately pre-processed and trained model. But existence of unimportant and irrelevant attributes in the training dataset may decrease the predictive accuracy and increase the time complexity in training phase. To increase the accuracy and efficiency, feature selection technique is frequently used in data mining. In this paper, a sequential forward selection based wrapper approach is proposed to select optimal and informative feature subset. It is known that diabetes mellitus is the most serious health problem and the complications lead to cause of death. So the aim of this research is to identify the significant attributes and classify diabetes dataset. The proposed approach is used to build the classifier models like Decision tree, K-Nearest Neighbor and Support Vector Machine produces the accuracies of 81.17%, 86.36% and 87.01% respectively. Finally, from results it is clear that the proposed model is performing better with high accuracy comparing the similar existing models. In the research, the Pima Indian diabetes dataset is used.

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Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines

Cited by 10 publications

References 22 publications

A Method for Classification Using Data Mining Technique for Diabetes: A Study of Health Care Information System

A Method for Classification Using Data Mining Technique for Diabetes: A Study of Health Care Information System

Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients

Developing Diabetes Disease Classification Model using Sequential Forward Selection Algorithm

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