An Integrated Artificial Intelligence and Machine Learning Approach for Real-Time Disease Prediction

Abstract

The rapid advancement of artificial intelligence has played a massive role in the medical and healthcare fields. Globally, disease prediction has been indispensable. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) technologies have had a significant impact on predicting or detecting the symptoms that can cause a huge loss in the future. Nevertheless, conventional approaches to disease comparison are resource-intensive in terms of both time and cost, whereas machine learning integrates deep learning techniques for more efficient analysis. Deep learning is a branch of machine learning that employs artificial neural networks to mimic how the human brain processes information, enabling it to identify patterns from large volumes of data. There is an intelligence system that analyses data to come up with valuable information for prediction purposes. This application focuses on the future possibilities and challenges, and harnessing technologies to develop pioneering public health solutions. The computers have the ability to perform calculations without any pre-programming through the help of machine learning technologies. Machine Learning uses the ideas of synthesis and induction to improve computers. It is employed in a variety of fields, especially bioinformatics and the diagnosis of diseases. Chronic illnesses, particularly cardiovascular diseases (CVDs), are among the leading causes of death worldwide, making it essential to develop accurate and timely prediction systems for early diagnosis and effective preventive care.