Comput Methods Programs Biomed - Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines.

Tópicos

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Resumo

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Na?ve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.

Resumo Limpo

studi propos novel predict approach human breast colon cancer use differ featur space propos scheme consist two stage preprocessor predictor preprocessor stage megatrend diffus mtd techniqu employ increas sampl minor class therebi balanc dataset predictor stage machinelearn approach knearest neighbor knn support vector machin svm use develop hybrid mtdsvm mtdknn predict model mtdsvm model provid best valu accuraci gmean matthew correl coeffici cancernoncanc dataset breastnonbreast cancer dataset colonnoncolon cancer dataset respect found hybrid mtdsvm best respect predict perform comput cost mtdknn model achiev moder better predict compar hybrid mtdnb nave bay expens higher comput cost mtdknn model faster mtdrf random forest predict better mtdrf best knowledg report result best result far dataset propos scheme indic develop model can use tool predict cancer scheme may use studi sequenti inform protein sequenc nucleic acid sequenc

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