Unveiling Insights: Harnessing AI for Lung Disease Detection and Classification
Angeline Lydia, K Antony Kumar · 2023
The public's health is greatly impacted by lung cancer, which is a serious global health concern. For a patient's prognosis, an early and precise diagnosis is essential. Machine learning has become a potent technique for predicting the prognosis of lung cancer. The goal of this work is to forecast the emergence of lung cancer by analysing a dataset of patient features and medical signs using various machine learning techniques. Models such as Logistic Regression (87.5% accuracy), Gaussian Naive Bayes (91.07% accuracy), Bernoulli Naive Bayes (91.07% accuracy), Support Vector Machines (85.71% accuracy), Random Forest Classifier (85.71% accuracy), K-Nearest Neighbours (92.86% accuracy), and XGBoost (91.07% accuracy) are used. Predictive frameworks are created by these models, which are evaluated using pertinent metrics after being trained on subsets of the dataset.