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

K Chandrashekar; Anagha S Setlur; Adithya Sabhapathi C; Satyam Suresh Raiker; Satyam Singh; Vidya Niranjan

doi:10.1177/11769351221147244

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

Cancer Inform. 2023 Jan 23:22:11769351221147244. doi: 10.1177/11769351221147244. eCollection 2023.

Authors

K Chandrashekar¹, Anagha S Setlur¹, Adithya Sabhapathi C¹, Satyam Suresh Raiker¹, Satyam Singh¹, Vidya Niranjan¹

Affiliation

¹ Department of Biotechnology, R V College of Engineering, Bengaluru, Karnataka, India.

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 incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.

Keywords: Cancer diagnosis; MCC; SMOTE; Web application; classification model; supervised machine learning.