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Breast cancer is one of the major death causing diseases of women around the globe.The best results achieve using the CFS-PSO with synthetic minority oversampling technique and random forest.The combination of many techniques can improve the models, we hybrid two meta-heuristic algorithms, namely particle swarm optimization (PSO) and genetic search methods.
Breast cancer is one of the major death causing diseases of women around the globe. The reported death occurs because of delays and inaccurate diagnosis. Therefore, high accuracy in cancer prediction models is important to improve treatment quality and survivability rate of patients. Cancer’s stage is an important predictor for any cancer type. This paper predicts the stage of breast cancer using data from surveillance epidemiology and end results (SEER). We use correlation-based feature selection (CFS) technique with different feature sets search methods to find the best relevant features. Non-convergent percentages of different stages reveal the unbalance data problem. This paper applies many balancing techniques to address this problem. Our model has two phases: feature selection and data balancing. We examine whether the order of phases have an influence on the results of our model. The combination of many techniques can improve the models, we hybrid two meta-heuristic algorithms, namely particle swarm optimization (PSO) and genetic search methods. We test the impact of hierarchical arrangement on the results. Arranging the synthesis search methods has negligible influence on the performance, it affect only the number of selected features neither accuracy nor time. The order of feature selection and sampling synthesis dramatically influence result of models. The best results achieve using the CFS-PSO with synthetic minority oversampling technique and random forest. The model builds in 22.375 seconds with 98.45% accuracy. The best order is feature selection following by data balancing. The results are promising and superior to those achieved with previous methods and demonstrate a potential in this field.
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