Improving Disease Diagnosis with Integrated Machine Learning Techniques

Abstract

As the digital transformation is constantly affecting every aspect of our lives, it is important to enhance and use machine learning models more effectively also in the healthcare domain. In this study, we focus on the application of machine learning algorithms for disease diagnosis in order to support decision making of physicians. Different classification methods are used to predict the diameter narrowing in the heart using an anonymous dataset. In order to increase the prediction ability of the machine learning algorithms, we employ different feature extraction methods such as Autoencoder, Stacked Autoencoder, Convolutional Neural Network, and Principal Component Analysis methods and integrate each feature extraction method with the classification methods. Then, we compare the prediction performances of individual and feature-extraction-integrated classification methods. It is shown that the prediction performance of the classification methods increase when integrated with feature extraction methods. However, it is concluded that not all feature extraction methods work as well with all classification methods. When a specific classification method is integrated with the appropriate feature extraction method, a better improvement in the prediction performance can be obtained.