A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis

Purnami, Santi Wulan; Embong, Abdullah; Zain, Jasni Mohamad; Rahayu, Sri

doi:10.3844/jcssp.2009.1003.1008

Cited by 31 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy reported in this study was 78.21% with 10-fold cross-validation. Purnami et al applied smooth support vector machines (SSVM) to the diabetes detection problem [27]. SSVM, developed by Lee et al, is an extension to SVM in which smoothing function is applied to solve the problem [28].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya¹,

Odedra²,

Samanta³

et al. 2010

Rev Diabet Stud

View full text Add to dashboard Cite

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

show abstract

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya¹,

Odedra²,

Samanta³

et al. 2010

Rev Diabet Stud

View full text Add to dashboard Cite

show abstract

“…A new SSVM for the problems of classification was proposed by Santi Waulan et al [3]. Radha and Rajagopalan, to diagnosis of diabetes introduced an application of fuzzy logic.…”

Section: Literature Surveymentioning

confidence: 99%

Accuracy, Sensitivity and Specificity Measurement of Various Classification Techniques on Healthcare Data

Gupta¹,

Rawal²,

Shiwani³

2013

IOSR-JCE

View full text Add to dashboard Cite

“…Ortiz et al (2012), proposed Self Organized Mapping (SOM) based on neural networks and Genetic Algorithm (GA) had been used to select the features sets. Loukas et al (2013) employed an integrated procedure consisting three pattern recognition algorithms (K-NN, SVM and PNN) for classifying and to characterize the breast cancer.Diabetes diagnosis had been with an accuracy of 93.2% as given by Purnami et al (2009). wavelet features have been considered in the works done by Sebri et al (2007).…”

Section: Jcsmentioning

confidence: 99%

Brain Tumor Classification Based on Clustered Discrete Cosine Transform in Compressed Domain

Anitha¹,

Murugavalli²

2014

Journal of Computer Science

View full text Add to dashboard Cite

This study presents a novel method to classify the brain tumors by means of efficient and integrated methods so as to increase the classification accuracy. In conventional systems, the problem being the same to extract the feature sets from the database and classify tumors based on the features sets. The main idea in plethora of earlier researches related to any classification method is to increase the classification accuracy.The actual need is to achieve a better accuracy in classification, by extracting more relevant feature sets after dimensionality reduction. There exists a trade-off between accuracy and the number of feature sets. Hence the focus in this study is to implement Discrete Cosine Transform (DCT) on the brain tumor images for various classes. Using DCT, by itself, it offers a fair dimension reduction in feature sets.Later on, sequentially K-means algorithm is applied on DCT coefficients to cluster the feature sets. These cluster information are considered as refined feature sets and classified using Support Vector Machine (SVM) is proposed in this study. This method of using DCT helps to adjust and vary the performance of classification based on the count of the DCT coefficients taken into account. There exists a good demand for an automatic classification of brain tumors which grealtly helps in the process of diagnosis. In this novel work, an average of 97% and a maximum of 100% classification accuracy has been achieved. This research is basically aiming and opening a new way of classification under compressed domain. Hence this study may be highly suitable for diagnosing under mobile computing and internet based medical diagnosis.

show abstract

A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis

Cited by 31 publications

References 13 publications

Computational Intelligence in Early Diabetes Diagnosis: A Review

Computational Intelligence in Early Diabetes Diagnosis: A Review

Accuracy, Sensitivity and Specificity Measurement of Various Classification Techniques on Healthcare Data

Brain Tumor Classification Based on Clustered Discrete Cosine Transform in Compressed Domain

Contact Info

Product

Resources

About