Assessment of Classification Algorithms in the Diagnosis of Diabetes and Breast Cancer

Seyed Abbas Mehdi Mahmoodi
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Abstract

Abstract — Nowadays medical sciences and physicians are faced with the volume of data. Since the diagnosis is not always easy, therefore the physician should consider results of the patient tests and decisions taken in the past for patients with similar conditions in order to make a good decision. In other words, the physician will need knowledge and experience. However, due to the large number of patients and any patient's multiple tests, the need for an automated tool to explore the former patients is felt. One of the important methods used to derive data is data mining. The aim of this paper is the application of classification algorithms for the diagnosis of diabetes and breast cancer and identifying the best algorithm. By comparing the obtained results, it turns out that there is no algorithm with maximum efficiency.

References

Polat K, Gunes S, Arslan A, A cascade learning system for classification of diabetes disease: Genetalized Discriminate Analysis and Least Square Support Vector Machine, Expert Systems with Applications, 2010: 34:482-487.

Andres C, Pena R., Sipper M, Designing Breast Cancer Diagnostic Systems via a Hybrid Fuzzy-Genetic Methodology, IEEE International Fuzzy Systems Conference, 2010: 1:135-139.

Howland J, Preventing Automobile Injury: New Findings From Evaluative Research. Dover, MA: Auburn House Publishing Company 1988:163–96.

Wu X, Kumar V, Quinlan J. R, et al. Top 10 Algorithms in Data Mining, Knowledge and Information Systems, 2009:14: 1-37.

Ganji M. F, Abadeh M. S, Parallel Fuzzy Rule Learning Using an ACO-Based Algorithm for Medical Data Mining, IEEE Fifth International Conference on Bio-Innspired Compting: theories and Applications, 2010: 573-581.

Ganji M. F, Abadeh, M. S, Using Fuzzy Ant Colony Optimization for Diagnosis of Diabetes Disease, Iranian conference Electrical Engineering, ICEE, 2011.

Ganji M. F, Abadeh, M. S, An Intelligence Fuzzy Classification System for Diabetes Detection, Iranian Conference on Fuzzy Systems, ifs, 2010

Abadeh, M. S, Habibi, J, Soroush, E, Induction of Fuzzy Classification Systems via Evolutionary ACO-Based Algorithms, International Journal of Simulation, Systems, Science, Technology, 2008: 9:1-8

Tsang C. H, Kwong S, wang H, Genetic-Fuzzy Rule Mining Approach and Evolution of Feature Selection Techniques for Anomaly Intrusion Detection, Pattern Recognition, 2009:40: PP. 2373-2391.


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