… disease) are picked up from UC Irvine machine learning … 84.07% accuracy is attained for
heart disease. For data set of … This study uses the UCI hepatitis patient data set. WEKA, tool …

… (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients
to solve some of these issues. Based on relevant research, this review explains how …

… The methodology described in this paper is diagnosing vulnerability of diabetic patients
to heart diseases and we had collected 500 records of diabetic patients to perform the …

… COPD patients are burdened with a daily risk of acute … In this study, we present a machine
learning-based strategy for … and clinically comprehensive set of patient cases to train a …

… ML can help with the timely care of patients, reduced future risk of disease, and streamlined
work processes. A conceptual diagnostic model is presented in this chapter, which is …

… of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic …
Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models …

… disease patients. We have to remark that for the patients with coronary artery disease the
two … disease patients, but are capable to present relevant information for those without these …

… the disease which is a supervised machine learning algorithm. The probability of the disease
… We are applying complete machine learning concepts to keep the track of patient’s health. …

… stages of CKD, patients often fail to notice the disease. Early … In this study, we propose a
machine learning methodology … California Irvine (UCI) machine learning repository, which has a …

… methodology that utilizes electronic medical records and machine learning to provide
personalized treatment recommendations for the management of coronary artery disease patients. …