Region based Support Vector Machine algorithm for medical diagnosis on Pima Indian Diabetes dataset

Karatsiolis, Savvas; Schizas, Christos N.

doi:10.1109/bibe.2012.6399663

Cited by 36 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They could achieve an accuracy of 82.05% [7]. S. Karatsiolis achieved 82.2% accuracy using modified support vector machine [14]. Mohammad Amine Chikh et al raised the mark to 82.69% in their work [15].…”

Section: Comparative Discussion Of Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System

Mottalib

Rahman

Habib

et al. 2016

TMLAI

View full text Add to dashboard Cite

Expert systems play an important role in medical diagnosis research. Researches are still being conducted for building expert systems capable of diagnosing different diseases. Diabetes mellitus is one of the diseases that have gained attention in the past years. Patients are usually unaware of having this disease and are finally diagnosed with diabetes after several years from onset. Since diabetes can be controlled, it is much desirable to harness it at the onset. Therefore, the prediction of onset of diseases like diabetes has been the point of interest for the researchers. Researchers are continuously trying to formulate an inference engine, a part of an expert system, in order to predict the disease at the beginning. In this paper, we present a Bayesian classification approach to identify the onset of diabetes mellitus in patients using a well-known data set as the sample. We have found an intriguing result with more than 87% accuracy.

show abstract

Section: Comparative Discussion Of Resultsmentioning

confidence: 99%

“…Classifier Accuracy [11] Bayes Belief Network 72.3% [10] Neural Network 76% [7] Support Vector Machine 82.05% [14] Modified Support Vector Machine 82.2% [15] Fuzzy K-Nearest Neighbor 82.69% [8] ANN and FNN 84.24% This paper Proposed Bayesian Classifier 87.28%…”

Section: Referencementioning

confidence: 99%

Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System

Mottalib

Rahman

Habib

et al. 2016

TMLAI

View full text Add to dashboard Cite

show abstract

“…Machine learning methods such as non-parametric treebased methods and support vector machines build a robust model with lower bias. Support Vector Machines were used to diagnose diabetes on Pima Indian data set in [5], [13]. Barakat The Diabetes Pedigree function provides some data about the diabetes history among the patient's relatives and the genetic relationship between that relative and the patient.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Classifiers for Prediction of Type II Diabetes Mellitus

Barhate

Kulkarni

2018

2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)

View full text Add to dashboard Cite

Diabetes mellitus is a chronic disease and a health challenge all over the world. As per the International Diabetes Federation, 451 million people have diabetes worldwide and this number is expected to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II Diabetes can be prevented or delayed by early identification of people at risk. Diabetes is difficult to diagnose in the early stages as the symptoms of the disease grow subtly and gradually. Many of the cases involve the patient being undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine and Neural network. Results demonstrate that random forests performed well on the data set giving an accuracy of 79.7%.

show abstract

“…Almost 28% of the total datasets are medical datasets. Therefore, there is an immense need to constantly improve the performance and disease predication accuracies [1].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, &amp; CART Ensembles

Bashir

Qamar

Khan

et al. 2014

2014 12th International Conference on Frontiers of Information Technology

View full text Add to dashboard Cite

Conventional techniques for clinical decision support systems are based on a single classifier or simple combination of these classifiers used for disease diagnosis and prediction. Recently much attention has been paid on improving the performance of disease prediction by using ensemble-based methods. In this paper, we use multiple ensemble classification techniques for diabetes datasets. Three types of decision trees ID3, C4.5 and CART are used as the base classifiers. The ensemble techniques used are Majority Voting, Adaboost, Bayesian Boosting, Stacking and Bagging. Two benchmark diabetes datasets are used from UCI and BioStat repositories respectively. Experimental results and evaluation show that Bagging ensemble technique shows better performance as compared to single as well as other ensemble techniques.

show abstract

Region based Support Vector Machine algorithm for medical diagnosis on Pima Indian Diabetes dataset

Cited by 36 publications

References 2 publications

Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System

Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System

Analysis of Classifiers for Prediction of Type II Diabetes Mellitus

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, &amp; CART Ensembles

Contact Info

Product

Resources

About

Region based Support Vector Machine algorithm for medical diagnosis on Pima Indian Diabetes dataset

Cited by 36 publications

References 2 publications

Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System

Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System

Analysis of Classifiers for Prediction of Type II Diabetes Mellitus

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, &amp;amp; CART Ensembles

Contact Info

Product

Resources

About

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles