Unleashing the Power of Feature Selection Techniques for High-Dimensional Cancer Data Classification

The world has seen a definite increase in the number of Cancer cases which also indicates that there is requirement of techniques for diagnosis of this disease with every possible information available. This study works on deciding upon how to implement feature selection methods on high dimensional cancer data so as to make them suitable for classification. Use of Machine Learning in medical field can be utilized to resolve the diagnosis process with the only available numeric data that can lead to faster detection and treatment. A comparative study of various normalization and feature selection techniques are included to select the most appropriate methods. Datasets are considered of various variety that includes both binary and multiclass classification datasets, so as to make the model acceptable for diverse situations in cancer field that is whether detecting presence of cancer cells, stages of cancer or it's subtypes. Finalized with four normalization and four feature selection techniques. And for classification purpose three machine learning algorithms, which are Logistic Regression, Random Forest and Support Vector Machine (SVM) are used to evaluate four genomic datasets. Multiple combinations of these are used to find the best performing algorithm to train the model prepared for classification.