Effective Machine Learning Techniques for Cancer Prediction – An Exploratory Study

Abstract

One of the most serious health issues in the human body is cancer which causes expansion of atypical cell. To enable the successive medical organization of patients, it is now essential in cancer research to detect and prognosticate the kind of cancer as quickly as possible. Therefore, Machine learning (ML) approaches is extensively used in cancer research for the construction of prediction models, resulting in efficient and precise decision-making for effectively diagnose cancer. It is imperative for ML tools can identify features in challenging datasets. In addition to ML methods, new tools for image processing have been created to identify cancer. Being able to clearly classify a biopsy image is a difficult undertaking since the pathologist needs to be familiar with the specific characteristics of both normal and damaged cells. This research work discussed utility of these various ML techniques to identify various cancer types has been examined in some prior research articles. Also, this study concludes the findings and a discussion of the analysis with evaluation methods used in chosen publications. The goal of this study was to identify challenges in the field of cancer prediction and determine the most likely future directions for research aimed at filling those gaps.