A combined AdaBoost and NEWFM technique for medical data classification

被引：10

作者：

Abuhasel, Khaled A. ^{[1
]}

Iliyasu, Abdullah M. ^{[1
,2
]}

Fatichah, Chastine ^{[3
]}

机构：

[1] College of Engineering, Salman Bin Abdulaziz University, P.O. Box 173, Al-Kharj

[2] Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology

[3] Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya

来源：

Lecture Notes in Electrical Engineering | 2015年 / 339卷

关键词：

Adaboost ensemble method; Biomedical engineering; Disease diagnosis; Fuzzy membership; Medical data classification; Neural network;

D O I：

10.1007/978-3-662-46578-3_95

中图分类号：

学科分类号：

摘要：

A hybrid technique combining the AdaBoost ensemble method with the neural network with fuzzy membership function (NEWFM) method is proposed for medical data classification and disease diagnosis. Combining the Adaboost, a general method used to improve the performance of learning methods, with the ‘standard’ NEWFM, which uses as base classifiers, ensures better accuracy in medical data classification tasks and diagnosis of diseases. To validate the proposal, four medical datasets related to epileptic seizure detection, Parkinson, cardiovascular (heart), and hepatitis disease diagnoses were used. The results show an average classification accuracy of 95.8% (made up of best accuracy of 99.5% for epileptic seizure, 87.9% for Parkinson, 97.4% for cardiovascular (heart) disease, and 98.7% for Hepatitis dataset classifications), which suggests that the proposed technique is capable of efficient medical data classification and potential applications in disease diagnosis and treatment. © Springer-Verlag Berlin Heidelberg 2015.

引用

页码：801 / 809

页数：8

共 19 条

[11]

Fatichah C., Tangel M.L., Et al., Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation, J. Of Adv. Comp. Intelligence and Intelligent Informatics, 16, 1, pp. 76-86, (2012)

[12]

Lim J.S., Et al., Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions, LNAI, 3614, pp. 811-820, (2005)

[13]

Lim J.S., Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system, Trans. On Neural Networks, 20, 3, pp. 522-527

[14]

Lee S.H., Lim J.S., Forecasting KOSPI based on a neural network with weighted fuzzy membership functions, Expert Systems with Applications, 38, pp. 4259-4263, (2011)

[15]

Schwenk H., Using Boosting To Improve A Hybrid HMM/Neural Network Speech Recognizer, Proc. Of IEEE Int. Conf. On Acoustics, Speech, and Signal Proc, 2, pp. 1009-1012, (1999)

[16]

Widyanto M.R., Fatichah C., Boosting with Kernel Base Classifiers for Human Object Detection, Asian J. Of Inf. Tech., 7, 5, pp. 183-190, (2008)

[17]

Freud Y., Scaphire R.E., A Short Introduction To Boosting, J. Of Japanese Society for Artificial Intell, 14, 5, pp. 771-780, (1999)

[18]

Bishop C.M., Pattern Recognition and Machine Learning, (2006)

[19]

Andrzejak R.G., Et al., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Physical Review E, 64, 6, pp. 1-8, (2001)

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