Developing an AI Model That Relies on Mobile Health Devices to Track Heart Activity

Hamdan H. Shehab; Fadi  Al-Turjman

doi:10.51173/jt.v6i3.2576

Authors

Hamdan H. Shehab Faculty of Engineering, Near East University, Nicosia (KKTC), Mersin 10, Turkey
Fadi Al-Turjman Faculty of Engineering, Near East University, Nicosia (KKTC), Mersin 10, Turkey

DOI:

https://doi.org/10.51173/jt.v6i3.2576

Keywords:

AI, Python Programming, TensorFlow, Google Colab, Simulation Data

Abstract

Heart disease lies among the top causes of death worldwide and accounts for a large number of deaths annually. Researchers are using artificial intelligence as a potent tool to construct cutting-edge healthcare applications in an effort to address this problem for the detection and avoidance of heart disease. This article presents the design and development of an artificial intelligence model using Python, TensorFlow, and Google Colab resources. Trained son simulation data with an 80:20 train/validation split and employing the Adam optimizer over 50 epochs, the model achieved an impressive 95% accuracy. Utilizing input data sumlation data from temperature, SPo2, heart rate and ECG signal the AI model predicts the individual's health state with a confidence level of 95%.

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Author Biographies

Hamdan H. Shehab, Faculty of Engineering, Near East University, Nicosia (KKTC), Mersin 10, Turkey

Department of Biomedical Engineering

Fadi Al-Turjman, Faculty of Engineering, Near East University, Nicosia (KKTC), Mersin 10, Turkey

Department of Artificial Intelligence Engineering

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