Optimised feature selection for early cancer detection

Global Cancer Incidence, Mortality and Prevalence (GLOBOCAN) status report

for the year of 2020, suggests the occurrence of 10.0 million cancer deaths

and 19.3 million new cancer cases. Clearly, cancer incidence and mortality

are rapidly growing worldwide. Also, the leading causes of cancer deaths are

found to be lung cancer and breast cancer. Cancer cells are having the

probability of spreading to other parts of the body too. Most chronic

cancers are not curable, but some can be controlled for a few months or

years. Also, there is a possibility of high rate of relapse of the disease.

These remissions can be partial or complete. But, if detected early, certain

cancers can be treated by surgery, chemotherapy, and radiation therapy. This

research work focuses on detecting cancer in its early stage so that right

measures can be taken to combat the disease. In this attempt to create a

beneficial working model, the combination of Artificial Neural Network

(ANN), Convolution Neural Network, Graph based Neural Network with Genetic

Algorithm (GA) have proven to be successful. As a proof of concept, we

present a combination of feature selection techniques that can effectively

reduce the feature set and optimize the classification techniques. The

proposed method, when applied on a benchmark dataset, gave a higher accuracy

by selecting most relevant 7 features out of 10 with an accuracy of 95.7%.

Using Convolution Neural Network, the accuracy improved to 98.3% with

optimal hyperparameter tuning.