Machine Learning-Based Multi-Cancer Diagnostic System for Early Detection and Accurate Classification Across Diverse Cancer Types

Abstract

Cancer is one of the major causes of death worldwide, mostly owing to late discovery and hence restricted treatment choices. Existing screening approaches are primarily invasive and often associated with complicated, long and expensive procedures. In biomedicine and bioinformatics, several research groups have examined the use of machine learning methods to solve the important challenge of categorizing cancer patients into high- and low-risk categories. These methodologies have thus been used to mimic the onset and treatment of cancer. The ability of ML algorithms to detect important characteristics in complex datasets further highlights their importance. Many of these approaches like as Decision Trees, Logistic Regression (LR), Support Vector Machines and K-Nearest Neighbours have been widely employed in cancer research to generate prediction models that aid decision makers to make better and more trustworthy decisions. ML methods are indeed able to improve our understanding of cancer formation, but need adequate validation to be regarded for application in ordinary clinical practice. Hence, an ML approach was utilized to simulate the progression of cancer. The prediction models shown here are based on several ML approaches and a broad variety of input features and Data Samples. The proposed framework incorporates data preprocessing, feature selection, and advanced classification algorithms to enhance diagnostic accuracy and facilitate timely clinical decision-making. The study emphasizes the potential of artificial intelligence in advancing precision oncology and improving healthcare outcomes.