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Abstract:The newer technologies such as data mining, machine learning, artificial intelligence and data analytics have revolutionized medical sector in terms of using the existing big data to predict the various patterns emerging from the datasets available inthe healthcare repositories. The predictions based on the existing datasets in the healthcare sector have rendered several benefits such as helping clinicians to make accurate and informed decisions while managing the patients’ health leading to better management of patients’ wellbeing and health-care coordination. The millions of people have been affected by the coronary artery disease (CAD). There are several machine learning including ensemble learning approach and deep neural networks-based algorithms have shown promising outcomes in improving prediction accuracy for early diagnosis of CAD. This paper analyses the deep neural network variant DRN, Rider Optimization Algorithm-Neural network (RideNN) and Deep Neural Network-Fuzzy Neural Network (DNFN) with application of ensemble learning method for improvement in the prediction accuracy of CAD. The experimental outcomes showed the proposed ensemble classifier achieved the highest accuracy compared to the other machine learning models. Keywords:Heart disease prediction, Deep Residual Network (DRN), Ensemble classifiers, coronary artery disease. 

Visualization explores quantitative content of data with human intuition and plays an integral part in the data mining process. When the data is big there are different analysis methods and approaches used to find inherent patterns and relationships. However, sometimes there is a need to incorporate human in loop approach to find new patterns and relationships. Immersive virtual reality (VR) offers a human centric approach to visualize data by discovering new relationships that an existing algorithm cannot provide. This paper demonstrates the data visualization tool to visualize Baltimore crime data in immersive environment and non-immersive environment. This paper focuses on VR visualization tool design, development, and usability assessment. A pilot user study was conducted for the VR visualization tool based on the system usability scale. The study results show that the crime data visualization tool is relatively easy to use, and that the application can be considered as a storyteller with removing the noise from the data and highlighting the usefulness of the information.