Predictive Analysis of Machine Learning and Deep Learning Algorithms for Lung Disease Prediction

Lung cancer is a significant health issue all over the world, and thus requires effective and precise solutions. Traditionally, lung cancer detection relied on primitive methods such as radiology examination of CT scans, which most of the time leads to delayed diagnosis. These primitive methods were error-prone, inconsistent, and might result in inaccurate results, hence making them hard. This paper discusses predictive analysis of machine learning and deep learning algorithms for prediction of lung cancer. In our approach, using both CSV data and CT scan images, we assess the accuracy and efficiency of different machine learning and deep learning models. While deep learning algorithms process images, machine learning algorithms are applied in CSV data. The paper combines machine learning and deep learning models with hyperparameter tuning to achieve the systems best performance. Evaluation metrics, such as accuracy, recall, precision, and F1-score, are utilized in the evaluation of the comparison purpose. Through model tuning and performance evaluation, the system seeks to uncover the most effective and efficient prediction model.