Key result
The proposed SHDML framework utilizing deep and machine learning techniques achieved an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87 for real-time heart disease detection.
Why the study?
Cardiovascular diseases are critical, making accurate analytics for real-time heart disease detection significant.
A novel smart healthcare framework combining wearable sensors and deep/machine learning algorithms demonstrated high accuracy (0.99) for real-time heart disease prediction.
Hypothesis-generating for ML-based real-time detection; prospective validation needed before clinical use.
Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.
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Elwahsh et al. (2021) studied Cardiovascular diseases (CVDs). Smart healthcare framework (SHDML) was evaluated on Accuracy of heart disease prediction. The proposed SHDML framework utilizing deep and machine learning techniques achieved an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87 for real-time heart disease detection.
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