Heart failure and other chronic diseases have posed huge challenges to the healthcare sectors across different country. As a result of inadequate tools and techniques to address the challenges, numerous patients have died while several others have experienced a decreased quality of life with various degrees of psychological burdens. With the revivification of artificial intelligence in the 1990s, computational intelligence (CI) tools have since been adopted in developing clinical decision support systems for heart disease diagnosis to ensure qualitative healthcare services. In this chapter, we describe some notable CI techniques and their clinical applications as it relate to heart failure risk prediction. Furthermore, the strength and limitation of these CI techniques in terms of accuracy and computational complexity are also discussed. Lastly, the potential role of CI in healthcare delivery systems is equally described. © 2018 Elsevier Inc. All rights reserved.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Guanglin Li

OW Samuel

GM Asogbon

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications

Effective prioritization of software project risks play an important role in determining whether the project will be successful in terms of performance characteristics or not. In this context, this study aims at hybridizing fuzzy multi criteria decision making approaches for the development of an assessment framework that can be used to efficiently identify and rank notable software project risks will aid decision making during the lifecycle of a software product. Specifically, for the assessment of project risk, we have integrated fuzzy Decision Making Trial and Evaluation Laboratory, Fuzzy Multi-Criteria Decision Making and TODIM (an acronym in Portuguese for Interactive and Multiple Attribute Decision Making) approaches. The proposed methodology is used for providing an effective rating mechanism to measure software risk factors. The effectiveness of the proposed approach results have been compared with earlier methods for the assessment of software project risks. The comparison was done based on the OMRON data set of 40 software projects. Evaluation results reveal that the integration of fuzzy approaches could be effective and as well accurate compared to existing approaches for evaluating software project risks. © 2017 Elsevier Ltd

Computers & Electrical Engineering

Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm

Heart attack, a complex health problem in which the electrical activity of the heart becomes chaotic due to extreme heart failure conditions, has been ranked the deadliest human diseases. Recent studies have reported that remote monitoring of patients with heart failure disease could help quantify their level of risks and provide useful information for efficient therapy. Additionally, such platforms could help increase accessibility to health care delivery at a relatively lower cost. Therefore this study proposed a context aware clinical decision support model using support vector machine (SVM) for heart failure risk prediction. The proposed model's performance was evaluated using dataset of potential heart failure patients with metrics including prediction accuracy, sensitive, specificity, and receiving operating characteristic (ROC). An average prediction accuracy of 87.9% and 82.0%, were respectively achieved for the training and testing sessions of the built Radial basis function (RBF) based SVM classifier with a sensitivity value of 76.9%. The results obtained from this study might aid the development of an efficient context aware clinical decision support systems in smart cities and the society at large. © 2017 Elsevier Ltd

Sustainable Cities and Society

Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis

Feature extraction is essential in Electromyography pattern recognition (EMG-PR) based prostheses control method. Time-domain features have been shown to have good performance in upper limb movement classification. However, the performance of EMG-PR prostheses driven by the existing time-domain features is still unsatisfactory. Hence, this study proposed three new time-domain features to improve the performance of EMG-PR based strategy in arm movement classification. EMG signals were recorded from the residual arms of eight amputees while performing different upper limb movements. Then, the newly proposed features were extracted and used to classify their limb movements. Experimental results showed that the proposed features could achieved an average classification accuracy of 92.00% ± 3.11% which was 6.49% higher than that of the commonly used time-domain features (p < 0.05). With three additional metrics, the proposed features also performed better, which suggest that the new features may be potential for improving the clinical performance of EMG-PR prostheses. © 2017 Elsevier Ltd

Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification

Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85–89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic. © 2016 Elsevier Ltd

Expert Systems with Applications

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

The offshore/onsite teams’ knowledge transfer (KT) effectiveness is one of the key determinants for achieving the outcome of global software development (GSD) projects. In this study, the significance of offshore/onsite teams (GSD teams) KT effectiveness in GSD projects is measured through various factors: knowledge, team, technology, and organization factors. Moreover, the assessment framework for the integration of knowledge, team, technology, and organization factors for evaluating KT effectiveness in GSD projects has not been adequately available in the existing literature. For this motivation, the main objective of this study is to propose the assessment framework to evaluate offshore/onsite teams KT effectiveness with reference to GSD project outcome. For evaluating KT effectiveness of GSD teams, we have integrated three Fuzzy Multi-Criteria Decision Making (FMCDM) methodologies: (a) Fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), (b) Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and (c) Elimination Et Choix Traduisant la REaite (ELECTRE). Further, the hybridization of fuzzy DEMATEL, TOPSIS, and ELECTRE has not available in the existing literature. Based on this research gap, we have integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating KT effectiveness of offshore/onsite teams in the context of GSD project outcome. Subsequently, the applicability and capability of proposed framework has been validated by software experts at Inowits Software Organization in India.

Journal	Data powered by TypesetComputational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications
Publisher	Data powered by TypesetElsevier
Open Access	0