Recent Trends in Healthcare System for Diagnosis of Three Diseases Using Health Informatics

Abstract

In recent days, critical diseases are taking precious lives of human beings. For the advance detection of these diseases, analysis and investigation of medical records can be favored. An automated tool is utilized for such disease classification task. This study concentrates on assessing the severity of kidney disease, heart disease, and liver disease at an early stage. For constructing the disease classification tool for each of the mentioned diseases, machine learning (ML)-based methods are employed. The proposed system focuses on providing an efficient disease detection with the highest efficiency and the lowest error rate. A comparative study among the well-known ML classifier models such as support vector machine, multilayer perceptron, k-nearest neighbor, decision tree, Naïve Bayes, gradient boosting, AdaBoost, and Random Forest is carried out in this research. For each of the considered diseases, the most appropriate model is chosen in terms of its prediction efficiency.