Application of multi-algorithm approach for lung cancer prediction

Abstract

Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at an advanced stage. Accurate and cost-effective early detection remains a major challenge due to the heterogeneity of imaging and histopathological features. Therefore, this study aimed to develop diagnostic software for lung cancer prediction using a multi- algorithm method. Patient data, including 16 clinical and lifestyle variables, were processed and analyzed with five machine learning algorithms, namely Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF), and Naïve Bayes (NB). Model performance was evaluated based on accuracy, precision, recall, and F1-score. The results showed that RF, NB, SVM, and NN achieved perfect predictive performance (100% across all metrics), while k-NN obtained slightly lower but still high performance (99%). These findings signified that multi-algorithm predictive modeling could provide robust diagnostic support for lung cancer detection. The proposed software offered potential as an accessible, low-cost decision-support tool to assist clinicians in early diagnosis and improve patient outcomes.