Diabetes Prediction Using Logistic Regression and K-Nearest Neighbor

Abstract

Diabetes is a long-term illness that has the ability to become a worldwide healthcare crisis. Diabetes mellitus, sometimes known as diabetes, is a metabolic disorder characterized by an increase in blood sugar levels. It is one of the world's most lethal diseases, and it is on the rise. Diabetes can be diagnosed using a variety of traditional approaches complemented by physical and chemical tests. Methods of data science have the potential to benefit other scientific domains by throwing new light on prevalent topics. Machine learning is a new scientific subject in data science that deals with how machines learn from experience. Several data processing techniques have been developed and utilized by academics to classify and predict symptoms in medical data. The study employs well-known predictive techniques such as K-nearest neighbor (KNN) and logistic regression. A predicted model is presented to improve and evaluate the performance and accuracy by comparing the considered machine learning techniques.