Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.

ABSTRACT: Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were in

SUBMITTER: Chandrashekar K 

PROVIDER: S-EPMC9880585 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature