Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients

TL;DR

This study evaluates the performance of different machine learning algorithms in predicting post-operative life expectancy for lung cancer patients, highlighting the most accurate models using clinical datasets.

Contribution

It provides a comparative analysis of multilayer perceptron, J48, and Naive Bayes algorithms for clinical prognosis in lung cancer, identifying the most effective classifier.

Findings

Multilayer perceptron achieved 82.3% accuracy.

J48 classifier achieved 81.8% accuracy.

Naive Bayes classifier achieved 74.4% accuracy.

Abstract

The nature of clinical data makes it difficult to quickly select, tune and apply machine learning algorithms to clinical prognosis. As a result, a lot of time is spent searching for the most appropriate machine learning algorithms applicable in clinical prognosis that contains either binary-valued or multi-valued attributes. The study set out to identify and evaluate the performance of machine learning classification schemes applied in clinical prognosis of post-operative life expectancy in the lung cancer patients. Multilayer Perceptron, J48, and the Naive Bayes algorithms were used to train and test models on Thoracic Surgery datasets obtained from the University of California Irvine machine learning repository. Stratified 10-fold cross-validation was used to evaluate baseline performance accuracy of the classifiers. The comparative analysis shows that multilayer perceptron performed best with classification accuracy of 82.3%, J48 came out second with classification accuracy of 81.8%, and Naive Bayes came out the worst with classification accuracy of 74.4%. The quality and outcome of the chosen machine learning algorithms depends on the ingenuity of the clinical miner.