Exploring the Efficacy of Machine Learning Classifiers in Lung Cancer Prognosis and Risk Assessment
Keywords:
Smoking history, Cancer diagnosis, Risk factors, early detection and occupational Hazards.Abstract
Lung cancer remains a significant public health challenge worldwide, with various risk factors contributing to its development. This study aims to investigate the association between age, smoking status, alcohol consumption, coughing, allergies, and lung cancer using a comprehensive dataset.
Objectives: Our analysis utilized a dataset containing information on individuals diagnosed with lung cancer, including demographic factors and lifestyle habits. Machine learning now days has a great influence to health care sector because of its high computational capability for early prediction of the diseases with accurate data analysis.
Method: The lung cancer prediction was analysed using different machine learning classification algorithms such as Naive Bayes, Random Forest (RF) and K- Nearest Neighbour (KNN) to boost the performance.
Findings: Among various metrics, we used accuracy and confusion matrix to analyze different machine learning classifiers to explore the relationships between these parameters and lung cancer incidence.
Novelty: These methodologies have been used to determine lung cancer patient survival rates and to assist clinicians in providing accurate prognosis. The key objective of this paper is the early diagnosis of lung cancer by examining the performance of classification algorithms. Finally this paper contributes understanding of the disease, improving diagnostic methods, developing more effective treatments, or offering new approaches for patient care.
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