Chronic Hepatitis Classification Using SNP Data and Data Mining Techniques

Uhmn, Saangyong; Kim, Dong-Hoi; Kim, Jin; Cho, Sung Won; Cheong, Jae Youn

doi:10.1109/fbit.2007.64

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…Ghumbre, Patil, and Ghatol (2011) applied SVM on heart disease diagnosis data. Uhmn, Kim, Kim, Cho, and Cheong (2007) presented SVM as one of the techniques to predict the susceptibility of the liver disease–chronic hepatitis–from single nucleotide polymorphism data. It has been compared with NB (Bapna & Gangopadhay, 2006).…”

Section: Alternative Methods and Lp Classifiermentioning

confidence: 99%

Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

et al. 2018

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In the last couple of decades, data analytics‐based pattern classification methods for disease detection have gained much traction in healthcare research and applications. The current study builds linear programming (LP) models for detecting disease incidence. We propose sequential steps of a convex programming algorithm to construct decision boundary functions to classify patterns in disease detection data. We compare the performance of our LP‐based classifier with others (neural network, decision tree, k‐nearest‐neighbor, logistic regression, naïve‐Bayes, and support‐vector‐machine) on four datasets: two different ones for breast cancer, and one each for diabetes and diabetic retinopathy. Statistical tests reveal that the LP classifier did significantly better than the other methods in five out of eight false‐positive and false‐negative test cases. There is not a statistically significant difference in performance in the remaining three tests between the LP classifier and the best alternative method. Most importantly, the LP classifier has significantly superior performance in both diabetes detection and diabetic retinopathy data. The success of the proposed LP classifier results from avoiding “modeling noise” and “memorization of training data.” We recommend that our proposed LP classifier be among the set of classifiers for use in disease detection analytics.

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Section: Alternative Methods and Lp Classifiermentioning

confidence: 99%

Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

et al. 2018

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“…The neural network is trained with the chosen significant patterns for the effective prediction of heart attack. Uhmn et al (2007;p.82) have presented the machine learning techniques, SVM, decision tree and decision rule to predict the vulnerability of the liver disease, chronic hepatitis from single nucleotide polymorphism data. The experimental results have shown that decision rule is able to distinguish chronic hepatitis from normal with the maximum accuracy of 73.20%, whereas SVM with 67.53% and decision tree with 72.68%.…”

Section: Related Workmentioning

confidence: 99%

Machine intelligence in stroke prediction

Jeena¹,

Kumar

2018

IJBRA

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The innovative developments in the field of machine intelligence have paved way to the growth of tools for assisting physicians in disease diagnosis. Early diagnosis and prognosis of stroke are crucial for timely prevention and cure. This research work focuses on the design of a stroke prediction system by investigating the various physiological parameters that are used as risk factors. Features extracted from various risk parameters carry vital information for the prediction of stroke. Classification algorithm that has been used with the number of attributes for prediction are support vector machines (SVM) and artificial neural network. Data collected from international stroke trial database was successfully trained and tested using both classifiers. The predictive models discussed here are based on different supervised machine learning techniques as well as on different input features and data samples. SVM gave an accuracy of 91% while neural network outperforms SVM by providing an accuracy of 98.1%.

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“…Tarek et al [27] proposed ensemble and hybrid intelligent techniques such as Support Vector Machine, Function Network and Fuzzy Logic to classify bioinformatics datasets. Saangyong et al [23] have presented the machine learning techniques, SVM, decision tree, and decision rule to predict the susceptibility of the liver disease, chronic hepatitis from single nucleotide polymorphism data. Ozyilmaz and Yildirim [22] have presented three neural network algorithms for diagnosis of hepatitis diseases, multilayer neural network produced significantly good result for the diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Myocardial Infarction Detection Using Intelligent Algorithms

Seenivasagam¹,

Chitra²

2016

NNW

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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Chronic Hepatitis Classification Using SNP Data and Data Mining Techniques

Cited by 15 publications

References 9 publications

Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

Machine intelligence in stroke prediction

Myocardial Infarction Detection Using Intelligent Algorithms

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