Predicting Heart Disease Using Collaborative Clustering and Ensemble Learning Techniques

Abstract

Different data types are frequently included in clinical data. Applying machine learning algorithms to mixed data can be difficult and impact the output accuracy and quality. This paper proposes a hybrid model of unsupervised and supervised learning techniques, which can be used in modelling and processing mixed data with an application in heart disease diagnosis. The model consists of two main components: collaborative clustering and combining decisions (the ensemble approach). The mixed data clustering problem is considered as a multi-view clustering problem; each view is processed using specialised clustering algorithms. Since each algorithm operates on a different space of the data set’s features, a novel collaborative framework was proposed that promotes the clustering process through information exchange between the different clustering algorithms, thereby producing expert models that model other spaces of the data set’s features. The expectation maximisation algorithm forms the foundation for this optimisation process, enhancing the collaborative term representing entropy; excellent convergence characteristics are therefore ensured. An ensemble approach similar to the stacking approach was used. The logistic regression model was utilised as a meta-classifier, training the expert model prediction results, and was subsequently used to predict the final output. The results prove the efficacy of this collaborative approach in optimising different clustering algorithms and meta-classifier outcomes.