Advancing Healthcare: Harnessing the Power of Machine Learning Algorithms for Accurate Disease Diagnosis in Clinical Settings through Statistical Models

Abstract

In the contemporary healthcare landscape, the timely diagnosis of life-threatening illnesses, such as liver diseases, cancer, heart conditions, and Alzheimer's, is pivotal for effective treatment and improved life expectancy. Early detection of diseases is crucial to intervene in a timely manner and enhance the overall well-being of individuals. This paper explores the application of machine learning, a subset of Artificial Intelligence, as a powerful tool for predictive modeling in clinical settings. Machine learning involves the development of algorithms that construct mathematical models from sample data, enabling predictions and decisions without explicit programming for the task. This research focuses on reviewing and analyzing various machine learning algorithms employed in clinical predictive modeling to achieve superior performance metrics such as accuracy, precision, recall, and F-measure.
In this research, a Long Short-Term Memory (LSTM) deep learning model is utilised to assess prediction accuracy, demonstrating its superiority over the Bayes model. This optimised feature selection process is achieved through the use of a Ranking-based Bee Colony method, which also improves classification accuracy. A deep learning approach for predicting breast cancer is shown, which manages database noise well.