ABSTRACT
Early and precise diagnosis of a range of fatal diseases such as cardiovascular, Alzheimer’s, renal, and hepatitis diseases is critical in enhancing patients’ long-term survival and saving millions of lives. Early strategies for predicting fatal diseases assisted in predicting the alterations that had arisen in extreme-risk patients, resulting in a decline in the risks. There is ample medical data within the healthcare industry that enhances the chances of incorporating the machine learning (ML) algorithms to assist efficaciously in the forecasting, detection, and management of diseases, thus reducing the rate of false assumptions. ML is a technique of artificial intelligence (AL) potentially utilized to address a multitude of challenges in information science. The machine gathers designs from the current dataset and transmits them to a cloud server for real-time processing, visualization, and diagnosis. This chapter represents an efficient and effective machine learning, including systems such as computer-aided diagnosis (CAD) systems relied on boosting algorithm, principal component analysis (PCA), artificial neural network (ANN), and support vector machine (SVM) to explore an accurate model of predicting numerous diseases such as hepatitis, heart, thyroid, and Alzheimer’s. In research, it was also demonstrated that using ML approach allows for the creation of well-proportioned organizational models that are rather more precise (86%) than the familiar synchrony metrics (83%) for the Alzheimer’s diseases. Through this chapter, the readers and researchers working in the domain will have a quick update about the significance and utilization of ML-based approaches to diagnose and cure several fatal diseases.
