EARLY DETECTION OF LUNGS CANCER USING MACHINE LEARNING ALGORITHMS

MR ANWAR; MA BAKAR; HM AWAIS; MU DIN; M MOHSIN; MA NAZIR; I SAQIB; MM KHALID

doi:10.54112/bcsrj.v2023i1.187

Authors

MR ANWAR Ripah International University Faisalabad, Pakistan
MA BAKAR Ripah International University Faisalabad, Pakistan
HM AWAIS Faisalabad Medical University, Allied Hospital Faisalabad, Pakistan
MU DIN Ripah International University Faisalabad, Pakistan
M MOHSIN Ripah International University Faisalabad, Pakistan
MA NAZIR Ripah International University Faisalabad, Pakistan
I SAQIB Ripah International University Faisalabad, Pakistan
MM KHALID Ripah International University Faisalabad, Pakistan

DOI:

https://doi.org/10.54112/bcsrj.v2023i1.187

Keywords:

lung cancer, stage, detection, accuracy

Abstract

Medical healthcare systems store a large amount of clinical data about patients related to their biographies and disease information. Doctors use clinical data for the early detection of diseases that helps with proper patients’ treatments to save their lives. These clinical systems are helpful in detecting cancer diseases at early stages to save people's lives. Lung cancer is the third largely spreading disease in human beings all over the globe, which may lead so many people to death because of inaccurate detection of their disease at the initial stages. Therefore, this study will help doctors and radiologists in the detection of lung cancerous and non-cancerous patients at early stages with a random forest algorithm to save patients’ lives. In this research work, a new and novel model based on random forest algorithm was employed to detect lung cancer from the Wisconsin data set. Lung cancer was detected at early stages, and it was decided whether targeted patient was cancerous or non-cancerous. This experimental outcome showed that the proposed methodology achieved an accuracy rate that was batter compared to previous studies for early detection of lung cancer.

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