Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence
Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using...
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/476 |
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| author | S. Manimurugan Saad Almutairi Majed Mohammed Aborokbah C. Narmatha Subramaniam Ganesan Naveen Chilamkurti Riyadh A. Alzaheb Hani Almoamari |
| author_facet | S. Manimurugan Saad Almutairi Majed Mohammed Aborokbah C. Narmatha Subramaniam Ganesan Naveen Chilamkurti Riyadh A. Alzaheb Hani Almoamari |
| author_sort | S. Manimurugan |
| collection | DOAJ |
| description | Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%. |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:34:31Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-8160a05c2a624029a1d23043fb9292eb2023-11-23T15:19:17ZengMDPI AGSensors1424-82202022-01-0122247610.3390/s22020476Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial IntelligenceS. Manimurugan0Saad Almutairi1Majed Mohammed Aborokbah2C. Narmatha3Subramaniam Ganesan4Naveen Chilamkurti5Riyadh A. Alzaheb6Hani Almoamari7Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaIndustrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaIndustrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaIndustrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USADepartment of Computer Science and IT, La Trobe University, Melbourne 3086, AustraliaFaculty of Applied Medical Sciences, University of Tabuk, Tabuk 47512, Saudi ArabiaFaculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi ArabiaInternet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.https://www.mdpi.com/1424-8220/22/2/476Internet of Medical Thingscloudheart disease predictionhybrid linear discriminant analysis with modified ant lion optimizationhybrid Faster R-CNN with SE-ResNet-101medical image |
| spellingShingle | S. Manimurugan Saad Almutairi Majed Mohammed Aborokbah C. Narmatha Subramaniam Ganesan Naveen Chilamkurti Riyadh A. Alzaheb Hani Almoamari Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence Sensors Internet of Medical Things cloud heart disease prediction hybrid linear discriminant analysis with modified ant lion optimization hybrid Faster R-CNN with SE-ResNet-101 medical image |
| title | Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence |
| title_full | Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence |
| title_fullStr | Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence |
| title_full_unstemmed | Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence |
| title_short | Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence |
| title_sort | two stage classification model for the prediction of heart disease using iomt and artificial intelligence |
| topic | Internet of Medical Things cloud heart disease prediction hybrid linear discriminant analysis with modified ant lion optimization hybrid Faster R-CNN with SE-ResNet-101 medical image |
| url | https://www.mdpi.com/1424-8220/22/2/476 |
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