Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey

The aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and best classification algorithm to analyse the specific disease. This study gives the details of different classification algorithms and feature selection methodologies. The study also discusses about the data constraints such as volume and dimensionality problems. This research paper also discusses the new features of C5.0 classification algorithm over C4.5 and performance of classification algorithm on high dimensional datasets. This research paper summarizes various reviews and technical articles which focus on the current research on Medical diagnosis.

[1]  J. Neapolitan.  Explaining Variation in Crime Victimization Across Nations and within Nations , 2003 .

[2]  Alok N. Choudhary,et al.  Poster: A lung cancer mortality risk calculator based on SEER data , 2011, 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS).

[3]  Hyunjung Shin,et al.  Predicting breast cancer survivability using fuzzy decision trees for personalized healthcare. , 2008, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[4]  G.Sophia Reena,et al.  Analysis of Liver Disorder Using Data mining Algorithm , 2010 .

[5]  Shweta Kharya,et al.  Using data mining techniques for diagnosis and prognosis of cancer disease , 2012, ArXiv.

[6]  Mohd Fauzi Othman,et al.  Comparison of different classification techniques using WEKA for breast cancer , 2007 .

[7]  H. Joensuu,et al.  Artificial Neural Networks Applied to Survival Prediction in Breast Cancer , 1999, Oncology.

[8]  Norman D. Black,et al.  Feature selection and classification model construction on type 2 diabetic patients' data , 2007, Artif. Intell. Medicine.

[9]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[10]  C. Jothi Venkateswaran,et al.  Estimating the Surveillance of Liver Disorder using Classification Algorithms , 2012 .

[11]  R. Srinivasa Perumal.  Analysis of Colon Cancer Dataset using K-Means based Algorithms & See5 Algorithms , 2011 .

[12]  Sheila Anand,et al.  Analysis of SEER Dataset for Breast Cancer Diagnosis using C4.5 Classification Algorithm , 2012 .

[13]  P. Sivaprakasam,et al.  IMPROVING THE PERFORMANCE OF K-NEAREST NEIGHBOR ALGORITHM FOR THE CLASSIFICATION OF DIABETES DATASET WITH MISSING VALUES , 2012 .

[14]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[15]  Biswadip Ghosh.  Using Fuzzy Classification for Chronic Disease Management , 2012 .

[16]  Arlindo L. Oliveira,et al.  A Data Mining Approach for the Detection of High-Risk Breast Cancer Groups , 2010, IWPACBB.

[17]  Dharminder Kumar,et al.  DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS , 2011 .

[18]  Dursun Delen,et al.  Predicting breast cancer survivability: a comparison of three data mining methods , 2005, Artif. Intell. Medicine.

[19]  Invariant Object Identification.  A Neural Network Model of , 2010 .

[20]  Sulabha S. Apte,et al.  Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques , 2012 .

[21]  U. G. Dailey.  Cancer,Facts and Figures about. , 2022, Journal of the National Medical Association.

[22]  Abdel-Badeeh M. Salem,et al.  Using data mining for assessing diagnosis of breast cancer , 2010, Proceedings of the International Multiconference on Computer Science and Information Technology.

[23]  Chih-Lin Chi,et al.  Application of Artificial Neural Network-Based Survival Analysis on Two Breast Cancer Datasets , 2007, AMIA.

[24]  Aboul Ella Hassanien,et al.  Rough Set Approach for Generation of Classification Rules of Breast Cancer Data , 2004, Informatica.

[25]  F. Paulin,et al.  Classification of Breast cancer by comparing Back propagation training algorithms , 2011 .

[26]  A. Santhakumaran,et al.  A Novel Classification Method for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks , 2010, 2010 International Conference on Data Storage and Data Engineering.

[27]  V. Sugumaran,et al.  ANALYSIS OF EEG SIGNALS USING DATA MINING APPROACH , 2012 .

[28]  Nadira Banu Kamal,et al.  Association Rule Mining based Decision Tree Induction for Efficient Detection of Cancerous Masses in Mammogram , 2011 .

[29]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[30]  Q. Zheng,et al.  Improvement of heart allograft acceptability associated with recruitment of CD4+CD25+ T cells in peripheral blood by recipient treatment with granulocyte colony-stimulating factor. , 2008, Transplantation proceedings.

[31]  Rae Woong Park,et al.  A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis , 2009 .

[32]  Chul-Heui Lee,et al.  Rule discovery using hierarchical classification structure with rough sets , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[33]  A. A. Safavi,et al.  Predicting breast cancer survivability using data mining techniques , 2010, 2010 2nd International Conference on Software Technology and Engineering.

[34]  F. Harrell,et al.  Artificial neural networks improve the accuracy of cancer survival prediction , 1997, Cancer.

[35]  Manaswini Pradhan,et al.  Predict the onset of diabetes disease using Artificial Neural Network (ANN) , 2011 .

[36]  J. Padmavathi,et al.  A Comparative study on Breast Cancer Prediction Using RBF and MLP , 2011 .

[37]  Chronic Disease Division.  Cancer facts and figures , 2010 .

[38]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .

[39]  S. Kannan,et al.  Classification Rule Construction Using Particle Swarm Optimization Algorithm for Breast Cancer Data Sets , 2010, 2010 International Conference on Signal Acquisition and Processing.

[40]  N. Jaisankar,et al.  Comprehensive Study of Heart Disease Diagnosis Using Data Mining and Soft Computing Techniques , 2013 .

[41]  Soni Jyoti,et al.  Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction , 2011 .

[42]  Krzysztof Michalak,et al.  Correlation-based Feature Selection Strategy in Neural Classification , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[43]  W. Wolberg,et al.  Pharmacokinetics of fluorouracil in humans. , 1978, Cancer research.

[44]  Amol P. Pande,et al.  Neural Network Aided Breast Cancer Detection and Diagnosis Using Support Vector Machine , 2006 .

[45]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[46]  K. Thangavel,et al.  Classification of Microcalcifications Using Multi-Dimensional Genetic Association Rule Miner , 2009 .

[47]  Erhan Guven,et al.  PREDICTING BREAST CANCER SURVIVABILITY USING DATA MINING TECHNIQUES , 2006 .

[48]  T. Gojobori,et al.  Midline governs axon pathfinding by coordinating expression of two major guidance systems. , 2009, Genes & development.

[49]  William Nick Street,et al.  A Neural Network Model for Prognostic Prediction , 1998, ICML.