Necessity of Machine Learning Algorithm in Health Issues: Prioritizing Lung Cancer Diagnosis

Abstract

Lung cancer is one of the leading causes of cancer-related deaths worldwide, underscoring the urgent need for early and accurate prediction to improve survival rates. This study presents a comparative analysis of the performance of various machine learning algorithms for lung cancer prediction. Several popular algorithms, including Support Vector Machines (SVM), Random Forest, k-Nearest Neighbors (k-NN), Logistic Regression, and Neural Networks, are evaluated using publicly available datasets. The models are assessed based on metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Key factors such as feature selection, data preprocessing, and hyperparameter tuning are also explored to optimize the performance of each algorithm. The results highlight the strengths and limitations of different techniques in handling complex lung cancer data, providing insights into the most suitable algorithms for clinical applications. This comparative approach aims to assist researchers and healthcare professionals in selecting robust models for early detection and personalized treatment strategies for lung cancer.