Abstract: In order to make decision making effective, large amounts of unmined healthcare data are collected from the health care industry used to discover hidden information. These insights and their correlations are in most cases not used to their optimum, and thus paving way to more advanced data mining techniques. Medical diagnosis using machine learning is a complex task that requires humongous amounts of data that traditional decision support systems cannot provide answers to. For instance, the likelihood of patients being diagnosed with heart disease can be predicted by using medical profiles such as sex, blood pressure, and sugar level which enables the establishment of significant knowledge such as patterns that exacerbates chronic heart failures. The quest to solve these problems led to the development of a user-friendly web-based application on the Microsoft .NET framework to serve as a Clinical Decision Support System (CDSS) for cardiologist.
Keywords: Machine Learning, K-Means, Naïve Bayes, Heart Disease, Clinical Support System
| DOI: 10.17148/IJARCCE.2020.9709