An Efficient IoT Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network
The leading causes of death worldwide are chronic illnesses suchlike diabetes, Heart Disease (HD) cancer as well as chronic respiratory malady. It is remarkably intricate to diagnose HD with disparate symptoms or features. With the augmentation in popularity of smart wearable gadgets, a chance to render an Internet of Things (IoT) solution has turned out to be more. Unfortunately, the survival rates are low for the people suffering from sudden heart attacks. Consequently, a patient monitoring scheme intended for heart patients utilizing IoT centered Deep Learning Modified Neural Network (DLMNN) is proposed to assist in the HD diagnosis, and medication is given accordingly. This proposed technique is executed via '3' steps: I) Authentication, ii) Encryption, and iii) Classification. First, by utilizing the substitution cipher (SC) together with the SHA 512, the heart patient of the specific hospital is authenticated. Subsequently, the wearable IoT sensor device, which is fixed to the patient's body, concurrently transmits the sensor data to the cloud. This sensor data is encrypted and securely transmitted to the cloud utilizing the PDH AES technique. After that, the encrypted data is finally decrypted, and by employing the DLMNN classifier, the classification is done. The classified outcomes comprise '2'types of data: i) normal and ii) abnormal. It denotes the patient's heart condition and if the outcome is abnormal, an alert text is passed to the physician for treating the patient. The investigational outcomes are estimated and the DLMNN for HD diagnosis shows improvement as compared to existing algorithms. Additionally, the proposed PDH AES used in support of secure data transmission results in the highest level of security i.e. 95.87% and it is achieved in the lowest time for encryption along with decryption when weighted against the existent AES.
IEEE Access