Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines.
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 (Naïve 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.
Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650 Islamabad, Pakistan. Electronic address: abdulmajiid@pieas.edu.pk.
Abdul
A
Majid
Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650 Islamabad, Pakistan. Electronic address: safdarali@pieas.edu.pk.
Safdar
S
Ali
Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650 Islamabad, Pakistan. Electronic address: mubashar@pieas.edu.pk.
Mubashar
M
Iqbal
Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650 Islamabad, Pakistan. Electronic address: nabeela.kausar@pieas.edu.pk.
Nabeela
N
Kausar
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D003110:Colonic Neoplasms / Q000175:diagnosis*; D019295:Computational Biology; D030562:Databases, Protein; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D060388:Support Vector Machine
Breast/colon cancer; K-nearest neighbor; Mega-trend diffusion; Naïve Bayes; Random forest; Support vector machines