An empirical analysis of decision tree algorithms: Modeling hepatitis data

Ramasamy, Manickam; Selvaraj, Shanthi; Mayilvaganan, M.

doi:10.1109/icetech.2015.7275013

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…Ensemble classifiers have been used in Neuroscience, proteomics and medical diagnosis. [13] proposed a modified random forest algorithm to calculate the accuracy of the classification algorithms on UCI liver dataset. It uses multi-layer perception classification algorithm and random subset feature selection method.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Machine Learning Techniques on Liver Disease - A Survey

V. Ramalingamdran,

Pandian,

Ragaven

2018

IJET

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A Liver infection causing Chronic Hepatitis B virus affects around 257 million people around the globe. About one million people who are infected from chronic infections like HBV die from chronic liver disease. Along these lines, there is a solid requirement for effective, exact and practical framework to foresee the result of such infection. It will be helpful in taking precaution steps and proper treatment. Machine learning assumes an essential job in restorative industry. Experts in machine learning today can guarantee an accurate and definite diagnosis and analysis of disease.Â Machine learning methodologies have been approached on various liver disease related datasets to predict outcome result. Machine learning calculations are exceptionally useful in giving essential measurements, continuous information, and progressed examination regarding the patients' illness, "lab test results, circulatory strain, family history, clinical preliminary information, and more to" specialists. Motivation behind this paper is to give an overview on relative survey on machine learning techniques that has been used on various liver disease datasets.Â Â

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Section: Ensemble Methodsmentioning

confidence: 99%

Machine Learning Techniques on Liver Disease - A Survey

V. Ramalingamdran,

Pandian,

Ragaven

2018

IJET

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“…By using different classifier they concluded that Random forest takes less running time with the highest accuracy of 87.50%. This accuracy gives help in ailment prediction and classification in the field of medical science [12].…”

Section: Related Workmentioning

confidence: 97%

Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases

Amjad¹,

Ali²,

Rafiq³

et al. 2019

IJACSA

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The classification and prediction of medical diseases is a cutting edge research problem in the medical field. The experts of machine learning are continuously proposing new classification methods for the prediction of diseases. The discovery of classification rules from medical databases for classification and prediction of diseases is a challenging and nontrivial task. It is very significant to investigate the more promising and efficient classification approaches for the discovery of classification rules from the medical databases. This paper focuses on the problem of selection of more efficient, promising and suitable classifier for the prediction of specific diseases by performing empirical studies on bunch mark medical databases. The research work under the focus concentrates on the benchmark medical data sets i.e. arrhythmia, breast-cancer, diabetes, hepatitis, mammography, lymph, liver-disorders, sick, cardiotocography, heart-statlog, breast-w, and lung-cancer. The medical data sets are obtained from the open-source UCI machine learning repository. The research work will be investigating the performance of Decision Tree (i.e. AdaBoost.NC, C45-C, CART, and ID3-C) and Support Vector Machines. For experimentation, Knowledge Extraction based on Evolutionary Learning (KEEL), a data mining tool will be used. This research work provides the empirical performance analysis of decision tree-based classifiers and SVM on a specific dataset. Moreover, this article provides a comparative performance analysis of classification approaches in terms of statistics.

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“…Kaur et al [ 17 ] drew a comparative analysis of many attribute selection techniques and used evaluation metrics like kappa statistic, accuracy, positive rate, and latency to analyze the performance. Ramasamy et al [ 18 ] applied decision tables, Hoeffding tree, logistic model tree (LMT), ensemble classifiers, and other trees to classify and compare risks of hepatitis. It was found that random forest recorded better performance than other algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

Mishra

Thakkar

Singh

et al. 2022

Computational Intelligence and Neuroscience

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Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in the precise assessment of chronic disorder risks in patients. Traditional attribute selection approaches suffer from premature convergence, high complexity, and computational cost. On the contrary, heuristic-based optimization to supervised methods minimizes the computational cost by eliminating outlier attributes. In this study, a novel buffer-enabled heuristic, a memory-based metaheuristic attribute selection (MMAS) model, is proposed, which performs a local neighborhood search for optimizing chronic disorders data. It is further filtered with unsupervised K-means clustering to remove outliers. The resultant data are input to the Naive Bayes classifier to determine chronic disease risks’ presence. Heart disease, breast cancer, diabetes, and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall, and F-score metric computed were 96.05%, 94.07%, and 95.06%, respectively. The model also has a least latency of 0.8 sec. Thus, it is demonstrated that chronic disease diagnosis can be significantly improved by heuristic-based attribute selection coupled with clustering followed by classification. It can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner.

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An empirical analysis of decision tree algorithms: Modeling hepatitis data

Cited by 10 publications

References 10 publications

Machine Learning Techniques on Liver Disease - A Survey

Machine Learning Techniques on Liver Disease - A Survey

Empirical Performance Analysis of Decision Tree and Support Vector Machine based Classifiers on Biological Databases

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

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