Disease Detection System (DDS) Using Machine Learning Technique

Abstract

AbstractIn this world, a human being suffers from many different diseases. Diseases can have a physical, but also a psychological impact on people. Mainly for four reasons, diseases are formed: (i) infection, (ii) deficiency, (iii) heredity and (iv) body organ dysfunction. In our society, doctors or medical professionals have the responsibility to detect and diagnose appropriate disease and provide medical therapies or treatments to cure or restrain the disease. Some diseases are cured after treatment, but chronic diseases are never cured despite the treatment; treatment can prevent chronic diseases to be worse over time. So, it is always important to detect and treat disease in early stage. To help doctors or medical professionals, this chapter proposes Disease Detection System (DDS) that can be used by doctors or medical professionals to detect diseases in patients using Graphical User Interface (GUI) of DDS. DDS is developed to detect some diseases such as Liver disorders, Hepatitis, Heart disease, Diabetes, and Chronic Kidney disease. Each of the diseases has different signs and symptoms among the patients. Different datasets are obtained from the Kaggle machine learning database to implement DDS. For the classification calculation, Adaboost Classifier Algorithm is used in DDS to detect diseases. This is a machine learning algorithm that results in the identification of referred diseases in DDS with 100% accuracy, precision and recall. The DDS GUI was created with the support of python as a screening tool so that doctors or medical professionals can easily detect patients with disease.KeywordsDiseases detectionDisease detection system (DDS)Adaboost classifier algorithmQuery from userResult from system