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Almulihi A, Saleh H, Hussien AM, Mostafa S, El-Sappagh S, Alnowaiser K, Ali AA, Refaat Hassan M. Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction. Diagnostics (Basel) 2022; 12:diagnostics12123215. [PMID: 36553222 PMCID: PMC9777370 DOI: 10.3390/diagnostics12123215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the "silent killer," is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper's aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.
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Affiliation(s)
- Ahmed Almulihi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
- Correspondence:
| | - Ali Mohamed Hussien
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
| | - Sherif Mostafa
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 34511, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Khaled Alnowaiser
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
| | - Abdelmgeid A. Ali
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Moatamad Refaat Hassan
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
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Nilashi M, Ali Abumalloh R, Alrizq M, Alghamdi A, Samad S, Almulihi A, Althobaiti MM, Yousoof Ismail M, Mohd S. What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology. Telematics and Informatics 2022; 69:101795. [PMID: 36268474 PMCID: PMC9556033 DOI: 10.1016/j.tele.2022.101795] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/14/2022] [Accepted: 02/21/2022] [Indexed: 10/31/2022]
Abstract
Social media users share a variety of information and experiences and create Electronic Word of Mouth (eWOM) in the form of positive or negative opinions to communicate with others. In the context of the COVID-19 outbreak, eWOM has been an effective tool for knowledge sharing and decision making. This research aims to reveal what factors of eWOM can influence travelers’ trust in their decision-making to travel during the COVID-19 outbreak. In addition, we aim to find the relationships between trust in eWOM and perceived risk, and perceived risk and the decision to travel. These relationships are investigated based on online customers’ reviews in TripAdvisor’s COVID-19 forums. We use a two-stage data analysis which includes cluster analysis and structural equation modeling. In the first stage, a questionnaire survey was designed and the data was collected from 1546 respondents by referring to the COVID-19 forums on TripAdvisor. Specifically, we use k-means to segment the users’ data into different groups. In the second stage, Structural Equation Modeling (SEM) was performed to inspect the relations between the variables in the hypothesized research model using a subsample of 679 respondents. The results of the first stage of the analysis showed that three segments could be discovered from the collected data for trust based on eWOM source and eWOM message attributes. These segments clearly showed that there are significant relationships between trust and perceived risk, and between perceived risk and the decision to travel. The results in all segments showed that users with a low level of trust have a high level of perceived risk and a low level of intention to travel during the COVID-19 outbreak. In addition, it was found that users with a high level of e-trust have a low level of perceived risk and a high level of intention to travel. These results were confirmed in all segments and these relationships were confirmed by SEM. The results of SEM revealed that visual and external information moderated the relationship between eWOM length and trust, and experience moderated the relationship between trust and perceived risk. For the moderating role of gender, it was found that the perceived risk has a higher impact on the decision to travel in the female sample.
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Althobaiti MM, Almulihi A, Ashour AA, Mansour RF, Gupta D. Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans. J Healthc Eng 2022; 2022:2872461. [PMID: 35070232 PMCID: PMC8769827 DOI: 10.1155/2022/2872461] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/10/2021] [Accepted: 12/17/2021] [Indexed: 12/18/2022]
Abstract
Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur's Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.
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Affiliation(s)
- Maha M. Althobaiti
- Department of Computer Science College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahmed Almulihi
- Department of Computer Science College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Amal Adnan Ashour
- Department of Oral & Maxillofacial Surgery and Diagnostic Sciences Faculty of Dentistry, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics Faculty of Science, New Valley University, El-Kharga 72511, Egypt
| | - Deepak Gupta
- Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
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Alazzam MB, Mansour H, Alassery F, Almulihi A. Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E-App. Comput Intell Neurosci 2021; 2021:5759184. [PMID: 35003245 PMCID: PMC8741365 DOI: 10.1155/2021/5759184] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/06/2021] [Accepted: 11/22/2021] [Indexed: 11/17/2022]
Abstract
Lifestyle influences morbidity and mortality rates in the world. Physical activity, a healthy weight, and a healthy diet are key preventative health behaviours that help reduce the risk of developing type 2 diabetes and its complications, such as cardiovascular disease. A healthy lifestyle has been shown to prevent or delay chronic diseases and their complications, but few people follow all recommended self-management behaviours. This work seeks to improve knowledge of factors affecting type 2 diabetes self-management and prevention through lifestyle changes. This paper describes the design, development, and testing of a diabetes self-management mobile app. The app tracked dietary consumption and health data. Bluetooth movement data from a pair of wearable insole devices are used to track carbohydrate intake, blood glucose, medication adherence, and physical activity. Two machine learning models were constructed to recognise sitting and standing. The SVM and decision tree models were 86% accurate for these tasks. The decision tree model is used in a real-time activity classification app. It is exciting to see more and more mobile health self-management apps being used to treat chronic diseases.
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Affiliation(s)
- Malik Bader Alazzam
- Faculty of Computer Science and Informatics, Amman Arab University, Amman, Jordan
| | - Hoda Mansour
- College of Business Administration, University of Business and Technology, Jeddah, Saudi Arabia
| | - Fawaz Alassery
- Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Ahmed Almulihi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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Alharithi F, Almulihi A, Bourouis S, Alroobaea R, Bouguila N. Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition. Sensors (Basel) 2021; 21:2450. [PMID: 33918120 PMCID: PMC8036303 DOI: 10.3390/s21072450] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/13/2022]
Abstract
In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation-maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.
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Affiliation(s)
- Fahd Alharithi
- College of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi Arabia; (A.A.); (S.B.); (R.A.)
| | - Ahmed Almulihi
- College of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi Arabia; (A.A.); (S.B.); (R.A.)
| | - Sami Bourouis
- College of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi Arabia; (A.A.); (S.B.); (R.A.)
| | - Roobaea Alroobaea
- College of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi Arabia; (A.A.); (S.B.); (R.A.)
| | - Nizar Bouguila
- The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC H3G 1T7, Canada;
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