Using neural networks to diagnose cancer
Journal of Medical Systems
The nature of statistical learning theory
The nature of statistical learning theory
Off-Line, Handwritten Numeral Recognition by Perturbation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers in Biology and Medicine
The class imbalance problem: A systematic study
Intelligent Data Analysis
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Computational Biology and Chemistry
A learning method for the class imbalance problem with medical data sets
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Computational Biology and Chemistry
Computational Biology and Chemistry
Cancer Classification from Gene Expression Data by NPPC Ensemble
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computational Biology and Chemistry
Computer Methods and Programs in Biomedicine
The classification of cancer stage microarray data
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Critical laboratory result reporting system in cancer patients
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Ultrasound based application for intraglandular mapping of breast cancer
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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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 (Naive 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.