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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.
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Estimation of the mean function of functional data via deep neural networks
In this work, we propose a deep neural networks‐based method to perform non‐parametric regression for functional data. The proposed estimators are based on sparsely connected deep neural networks with rectifier linear unit (ReLU) activation function. We provide the convergence rate of the proposed deep neural networks estimator in terms of the empirical norm. Through Monte Carlo simulation studies, we examine the finite sample performance of the proposed method. Finally, the proposed method is applied to analyse positron emission tomography images of patients with Alzheimer's disease obtained from the Alzheimer Disease Neuroimaging Initiative database.
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- Award ID(s):
- 1736470
- PAR ID:
- 10446369
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Stat
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2049-1573
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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