Abstract
The HealthCare (HC) industry has to maintain a large sum of critical medical data. For securing the storage of those data, BlockChain (BC) is used; however, the authorized user cannot easily access the original data, and it also encompasses a storage capacity issue. Although the external DataBase (DB) in BC technology is considered by current research methodologies, it is unsecure. To tackle those issues, this paper secured the diagnosed Parkinson's Disease (PD) data by BC technology utilizing the 2E2C algorithm. This work takes the Parkinson's user data to store in BC and detect the status of the patients. Initially, authentication is carried out. After that, the encryption of the data is done by the Enhanced Elliptic Curve Cryptography (2E2C) algorithm. Next, the data are amassed in the external DB of BC by the user's gateway. Initially, Hospital Management (HM) downloads the data as of the BC and then requests the patient to access the private key for decrypting the data. After that, data pre-processing and Feature Extraction (FE) re performed, then the ISO algorithm selects the vital features. These features are inputted to the Modified Extreme Learning Machine (MELM), which classifies the user's health status, and the status is stored in the BC; the status is also encrypted. An alert message is sent to the client app if any critical stage is presented in the detection outcome. In an experimental appraisal, the proposed methods' performance is contrasted with the prevailing research methods.