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

Seyed Abbas Mahmoodi; Maryam Sadat Mahmoodi; Vahide Sadat Abbasniya; Seyed Mostafa Mahmoodi

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

Seyed Abbas Mahmoodi, Maryam Sadat Mahmoodi, Vahide Sadat Abbasniya, Seyed Mostafa Mahmoodi

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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.

Keywords

Data Mining; Classification; Diabetes; Breast Cancer;

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 JR. Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 2009; 14: 1-37.

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

Ganji MF, Abadeh MS. An Intelligence Fuzzy Classification System for Diabetes Detection. Iranian Conference on Fuzzy Systems, 2010.

Abadeh MS, 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 CH, Kwong S, Wang H. Genetic-Fuzzy Rule Mining Approach and Evolution of Feature Selection Techniques for Anomaly Intrusion Detection. Pattern Recognition, 2009; 40: 2373-2391.

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

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