Diabetes prediction using machine learning technique
Abstract
Diabetes is a biological disorder that impacts people in almost every country and, if not discovered early, can lead to significant complications like stroke, kidney failure, and premature death. To combat this, a number of researchers are attempting to predict diabetes at an early stage using a variety of ways. Diabetes is diagnosed by a range of widely available traditional techniques based on physical and substance tests. Many people who have diabetes don‘t know about it. There are currently about 420 million people living with diabetes. According to World Bank data, the Marshall Islands has the highest diabetes prevalence rate, with 3:10 persons suffering from the disease. The extraction of diabetes feature information and the medical record for the diagnosis and treatment of sickness becomes more important to stimulate the growth of diabetes prediction and community medicine. Machine learning, data mining, artificial neural networks, fuzzy structures, genetic algorithms, rough collection, and various algorithms are among the methodologies and implementations for diabetes prediction [1]. Different state-of-the-art diabetes prediction approaches are given and compared in this research, as well as the machine learning techniques they used. Diabetes is a chronic disease so its prediction is necessary for saving lives. This survey paper helps to find out some previous literature limitation and help out to narrow down the importance of diabetes prediction. The major steps for prediction is data-preprocessing, feature selection, data splitting, apply various machine learning techniques to train data and get prediction results for disease data sets.