IOT-DRIVEN HEART DISEASE PREDICTION WITH INTELLIGENT CLASSIFIER AND SQUIRREL SEARCH FEATURE SELECTION

Abdulbasit A. Darem, Manal Abdullah Alohali, Siwar Ben Haj Hassine, Belal Zaqaibeh, Majed Aborokbah and Ahmed S. Salama
Additional contact information
Abdulbasit A. Darem: Department of Computer Science, College of Science, Northern Border University, Arar 91431, Saudi Arabia
Manal Abdullah Alohali: ��Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Siwar Ben Haj Hassine: ��Department of Computer Science, Applied College at Mahayil, King Khalid University, Guraiger, Abha 62521, Saudi Arabia
Belal Zaqaibeh: �Faculty of Science and Information Technology, Jadara University, Irbid, Jordan
Majed Aborokbah: �Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia
Ahmed S. Salama: ��Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-15

Abstract: Cardiovascular disease (CVD) is the leading cause of global mortality in the modern world. This situation is difficult to predict and requires a combination of advanced techniques and specialist knowledge. Healthcare systems have recently adopted the Internet of Things (IoT) to collect critical sensor data to diagnose and predict CVD. Predictive models can be made more accurate and effective through such integration, which could radically change how we manage cardiovascular health. This study presents an improved squirrel search optimization algorithm for searching vital indications of CVD. To address the issue of low-cardiac diagnostic accuracy, the proposed IoT system uses enhanced squirrel search optimization with deep convolutional neural networks (SSO-DCNN). This new approach uses data from smartwatches and cardiac devices, which monitor patients’ electrocardiogram (ECG) and blood pressure readings. The proposed SSO-DCNN performs well compared to well-known deep learning networks such as logistic regression. The findings show an accuracy of 99.1% over current classifiers, suggesting effectiveness in the CVD prediction.

Keywords: Cardiovascular Disease; Internet of Things; Squirrel Search Optimization; Deep Convolutional Neural Network; Electrocardiogram (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25400055

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