Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis
- 338 Downloads
In this paper, we introduce a novel hybrid method that uses a genetic algorithm (GA) in combination with a swarm optimization algorithm (particle swarm optimization (PSO) or fruit fly optimization algorithm (FOA)) for medical diagnosis. The proposed approaches, called GAPSO-FS and GAFOA-FS, simultaneously employ the genetic algorithm (GA) to choose the optimal feature subset and the swarm optimization algorithms (PSO/FOA) to optimize the SVM parameters. This procedure primarily comprises three synchronized parallel layers, including two optimization layers and an intermediate layer. The intermediate layer is mainly responsible for harmonizing the information from the two optimization layers and then distributing the processed information back to those layers. The major contribution of the proposed approaches is that they fully exploit the advantages of the different algorithms. The genetic algorithm (GA) excels at selecting the optimal feature subset, whereas swarm optimization algorithms (PSO/FOA) are optimal for searching the most appropriate continuous variables, including the penalty parameter C and the hyperplane parameter. We performed several groups of experiments on real-world medical cases from the UCI machine learning data repository to compare our hybrid approaches with well-known optimization techniques. The empirical results demonstrated that the proposed GAPSO-FS and GAFOA-FS can select the best SVM model parameters and a more highly relevant feature subset for the SVM classifier than a single algorithm can, thus improving the classification performance when solving a medical diagnosis problem. Therefore, the proposed approach has potential as a useful tool in medical diagnosis.
Keywordsfeature selection medical diagnosis fruit fly optimization particle swarm optimization SVM optimization
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal studies
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
- 1.Achayuthakan C, Ongsakul W (2009) TVAC-PSO based optimal reactive power dispatch for reactive power cost allocation under deregulated environment. In IEEE Power & Energy Society (PES) General Meeting ‘09, Calgary, pp 1–9Google Scholar
- 3.Anto S, Chandramathi S, Aishwarya S (2016) An expert system based on LS-SVM and simulated annealing for the diagnosis of diabetes disease. Int J Inf Commun Technol 9:88–100Google Scholar
- 4.Bhullar PS, Dhami JK (2016) PSO-TVAC-based economic load dispatch with valve-point loading. In Proceedings of Fifth International Conference on Soft Computing for Problem Solving ‘16, Singapore, pp 823–832Google Scholar
- 5.Bouchlaghem R, Elkhelifi A, Faiz R (2015) SVM based approach for opinion classification in Arabic written tweets. In IEEE/ACS 12th International Conference of Computer Systems and Applications ‘15, Marrakech, pp 1–4Google Scholar
- 12.Eggermont J, Kok JN, Kosters WA Genetic programming for data classification: partitioning the search space. In Proceedings of the 2004 ACM Symposium on Applied Computing ‘04, Nicosia, pp 1001–1005Google Scholar
- 16.Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In IEEE International Conference On Systems, Man, and Cybernetics, Computational Cybernetics and Simulation’97, Orlando, pp 4104–4108Google Scholar
- 25.Selvathi D, Emala T (2016) MRI brain pattern analysis for detection of Alzheimer’s disease using random forest classifier. Intell Decis Technol 1–10. doi: 10.3233/IDT-160260D
- 26.Shen L, Chen H, Kang W, Gu H, Zhang B, Ge T (2015) Fruit fly optimization algorithm based SVM classifier for efficient detection of Parkinson’s disease. In International Conference on Swarm Intelligence ‘15, Beijing, pp 98–106Google Scholar
- 28.Street WN, Wolberg WH, Mangasarian OL (1993) Nuclear feature extraction for breast tumor diagnosis. In International Symposium on Electronic Imaging: Science and Technology ‘93, San Jose, pp 861–870Google Scholar
- 29.Varshney S, Srivastava L, Pandit M (2011) Optimal location and sizing of STATCOM for voltage security enhancement using PSO-TVAC. In IEEE International Conference on Power and Energy Systems ‘11, Chennai, pp 1–6Google Scholar
- 33.Weronika Piątkowska MJ, Martyna J, Nowak L, Przystalski K (2011) A decision support system based on the semantic analysis of melanoma images using Multi-elitist PSO and SVM/machine learning and data mining in pattern recognition. In International Workshop on Machine Learning and Data Mining in Pattern Recognition ‘11, New York, pp 362–374Google Scholar
- 35.Zainuddin N, Selamat A, Ibrahim R (2016) Twitter feature selection and classification using support vector machine for aspect-based sentiment analysis. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems ‘16, Morioka, pp 269–279Google Scholar