Year: 2024
Type: article
Abstract: The human lungs, crucial for supplying oxygen, are vulnerable to diseases such as lung cancer, a leading cause of mortality. Timely prediction of lung cancer is essential to enable early intervention by healthcare professionals, enhancing patient outcomes and saving lives. This study introduces a comprehensive Machine Learning (ML) model designed to predict lung cancer at an early stage, utilizing a dataset sourced from Kaggle. Built on the Random Forest algorithm, the model assesses a diverse set of characteristics and variables, including gender, age, and exposure to various environments and lifestyles. It accurately identifies individuals at a higher risk of developing early-stage lung cancer, facilitating prompt intervention and personalized treatment strategies. Key evaluation metrics demonstrating the model's effectiveness include precision, F1 score, recall, and accuracy. The findings indicate a model accuracy of approximately 97.9%, underscoring its potential as a valuable tool for enhancing the early detection of lung cancer.
Source: Discover Artificial Intelligence
Authors: Yusupha Sinjanka, Veerpal Kaur, Usman Ibrahim Musa, Karandeep Kaur
Institution: Lovely Professional University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,490
OpenAlex ID: W4404730099