Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.

In the fast-growing field of medicine and its dynamic demand in research, a study that proves significant improvement to healthcare seems imperative especially when it is on cancer research. This research paved way to such significant findings by the inclusion of feature selection as one of its major components. Feature selection has become a vital task to apply data mining algorithms effectively in the real-world problems for classification. Feature selection has been the focus of interest for quite some time and much completed work related to it. Although much research conducted on the field, a study that proved a nearly perfect accuracy seems limited; hence, more scientifically driven results should be produced. Using various research on feature selection as basis for the choices in this study, the method was product of careful selection and planning. Specifically, this study used feature selection for improving classification accuracy on cancerous dataset. This study proposed Artificial Neural Network (ANN) for cancer classification with feature selection on colon cancer dataset. The study used best first search method in weka tools for feature selection. Through the process, a promising result has been achieved. The result of the experiment achieved 98.4 % accuracy for cancer classification after feature selection by using proposed algorithm. The result displayed that feature selection improved the classification accuracy based on the experiment conducted on the colon cancer dataset. The result of this experiment was comparable with the other studies on colon cancer research. It showed another significant improvement and can be considered promising for more future applications.

Ovarian cancer is a severe disease for older woman. Based on the research, ovarian cancer is the fifth commonly disease and the seventh causes of death for woman worldwide. For ovarian cancer classification problem, many researchers have performed using Artificial Neural Network (ANN). Classification accuracy is a significant factor for taking decision by the Doctors. Higher classification accuracy can help to take the decision by doctors for giving proper treatment. Accurate and early diagnosis can save lives and reduce the percentage of mortality. This study focuses classification accuracy analysis of ovarian cancer. The purpose of this study is to analyze the classification accuracy using 15-neuron ANN model. The proposed model is benchmarked on ovarian cancer dataset. The achieving result from the proposed model has been compared with the other four classification algorithms. The proposed model has achieved 98.7% ovarian cancer classification accuracy which is more promising and higher than other classification algorithms.

An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers.