Clinical decision support systems using machine learning algorithms for lifestyle diseases for improved prediction efficiency

Abstract

n humans, Lifestyle Disease (LSD) is caused due to improper newlineway of life such as less physical activity, sleeplessness, unhealthy eating newlinehabits, liquor drinking, and smoking. LSD leads to gastric problems, newlineindigestion of food, and prognosis to heart problems, Type II newlinediabetes, and lung diseases. LSD treatment and medication lead to high newlineexpenditure for patients and country through LSD management and newlinepolicies. Patients who suffer from LSD need lifelong treatment. The newlinesolution to reducing mortality due to Lifestyle Diseases is early detection newlineand effective treatment. LSDs are low progressive in nature and need an newlineeffective and accurate early prediction method for effective treatment. newlineIn developing countries, people suffer from Heart Disease (HD) newlineand diabetes, which are the major diseases of LSD. Diabetes have newlineincreased over the world and become a chronic disease. Diabetes and HD newlineare highly correlated and cause morbidity and mortality in LSD patients. newlineHeart disease patients have the following symptoms such as obesity, newlineincrease in pulmonary heart pressure, hypertension and dyslipidemia, and newlinethe above symptoms are also common in diabetic person. Diabetics newlineperson are prone to cardiovascular disease (CVD) and mortality newline