Aim/PurposeThis study aims to develop a predictive model using extensive patient records to forecast lung cancer outcomes. Lung cancer is a severe international fitness difficulty that still exists because the outcomes of sufferers differ significantly from each other. Predictive modeling has a massive promise to improve and better comprehend ailment outcomes. In this work, we use ensemble studying techniques – particularly Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Timber (DT) – to determine the direction of lung cancer ailment, so addressing the pressing need for specific prognostic gear.

BackgroundLung cancer is a severe international fitness difficulty that still exists due to the fact the outcomes of sufferers differ significantly from each other. Predictive modeling has massive promise to improve and better comprehend ailment outcomes. In this work, we use ensemble studying techniques—particularly Artificial Neural Networks (ANN), aid Vector Machines (SVM), and decision timber (DT)—to determine the direction of lung cancer ailment, so addressing the pressing need for specific prognostic gear.

MethodologyThe proposed approach employs an ensemble learning algorithm to improve predictive accuracy. The dataset permits a thorough evaluation of the prognosis of most lung cancers because it has an extensive range of clinical factors, patient demographics, and molecular markers.

ContributionThe issue of predicting the path of most lung cancers is complicated because of the intricate relationship of several medical, genetic, and environmental elements. This work improves the degree of accuracy and dependability required for treatments.

FindingsThe study suggests that ensemble studying helps forecast lung cancer sickness, and the proposed model outperforms traditional techniques with its better sensitivity, accuracy, and specificity.