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Blockchain Assisted Disease Identification of COVID-19 Patients with the Help of IDA-DNN Classifier
C B Sivaparthipan1, Bala Anand Muthu1, G Fathima1
1Department of Computer Science, Adhiyamaan College of Engineering, Hosur, India.
Insights
A new blockchain-assisted deep learning algorithm, the Improved Dragonfly Algorithm-Deep Neural Network (IDA-DNN), effectively detects post-COVID-19 diseases. This method enhances diagnostic accuracy for recovered patients experiencing persistent symptoms.
Area of Science:
- Computer Science
- Artificial Intelligence
- Medical Informatics
Background:
- Millions affected by COVID-19, with some experiencing prolonged symptoms post-recovery.
- Accurate detection of post-COVID-19 conditions is crucial for patient management.
- Existing diagnostic methods may not fully address the complexity of long-term COVID-19 symptoms.
Purpose of the Study:
- To propose a novel blockchain-assisted deep learning algorithm for detecting diseases in COVID-19 recovered patients.
- To enhance the accuracy and security of patient data in disease detection.
- To develop an efficient method for identifying persistent symptoms after COVID-19 recovery.
Main Methods:
- A blockchain (BC) was used to securely store post-symptom data of recovered COVID-19 patients.
- An Improved Dragonfly Algorithm-Deep Neural Network (IDA-DNN) was developed for disease detection.
- The IDA-DNN model underwent training using four diverse datasets, including preprocessing, feature extraction, and reduction.
Main Results:
- The IDA-DNN model demonstrated efficient classification of diseases in recovered COVID-19 patients.
- The algorithm successfully distinguished between the presence and absence of disease.
- Comparative analysis showed the proposed IDA-DNN outperformed existing techniques in COVID-19 detection.
Conclusions:
- The proposed IDA-DNN algorithm offers a secure and effective approach for detecting post-COVID-19 diseases.
- Blockchain integration enhances patient data security in the diagnostic process.
- This novel deep learning model shows significant promise for improving the management of long-term COVID-19 complications.
Abstract:
Globally, millions of people were affected by the Corona-virus disease-2019 (COVID-19) causing loads of deaths. Most COVID-19 affected people recover in a few spans of weeks. However, certain people even those with a milder variant of the disease persist in experiencing symptoms subsequent to their initial recuperation. Here, a novel Block-Chain (BC)-assisted optimized deep learning algorithm, explicitly improved dragonfly algorithm based Deep Neural Network (IDA-DNN), is proposed for detecting the different diseases of the COVID-19 patients. Initially, the input data of the COVID-19 recovered patients are gathered centered on their post symptoms and their data is amassed as a BC for rendering security to the patient's data. After that, the disease identification of the patient's data is performed with the aid of system training. The training includes '4' disparate datasets for data collection, and then, performs preprocessing, Feature Extraction (FE), Feature Reduction (FR), along with classification utilizing ID-DNN on the gathered inputted data. The IDA-DNN classifies '2' classes (presence of disease and absence of disease) for every type of data. The proposed method's outcomes are examined as well as contrasted with the other prevailing techniques to corroborate that the proposed IDA-DNN detects the COVID-19 more efficiently.
