# Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing
## 3 Proposed Methodology
### Proposed Algorithm for Large-Scale Clinical Information Sharing (LSCIS)  
The user provides the input as a waiting period and canvas period, and the in-built server checks for the entire system and updates the Consensus Model (CM), which will be accessed by the user C node. The error is computed and the blockchain is checked using the CM. Additionally, the evaluation of the Consensus Model generated by the user C node and the computation [23] of the error takes place. The transaction operation is applied from the CIS, the hash function is applied with the updated signature, and the error rate ER is calculated. Upon checking the threshold value of the error rate, the transaction performed and the distributed structured [24] data are shared among different hospitals and healthcare centers.  
Figure 4: Block diagram of the blockchain-based hash function.  
Proposed algorithm steps:  
Input: User (application used to share clinical information), canvas time period (CTP), and waiting period (WP)  
Output: Advanced Al model (AIM)  
* Step 1: Checking blockchain canvas time period (CTP)
* Step 2: If (applying the updated consensus model (CM))
* Step 3: Access the updated modern consensus model (CM) generated by the user C node, compute the error, and check the blockchain with the CM.
* Step 4: Evaluate the performance of the CM on Z, and calculate the error ER.
* Step 5: Perform the transaction operation CIS, apply hash function \(\text{hash}=\text{HASH}\) (CIS), and then compute the error (ER).
* Step 6: If (information is the transfer between one node and another)
* Step 7: Perform the update operation node with CIS information to produce the novel CM model to compute the new error (ER).
* Step 8: Apply and update New CM = Old CM
* Step 9: Perform the transaction operation CIS to CIS and apply hash function \(\text{hash}=\text{HASH}\) (CIS); then, compute error (ER).
* Step 10: Canvas time period (CTP), perform waiting and accumulate error rate (from other nodes).
* Step 11: Check that the ER is not higher than the error
* Step 12: Determine the node with the highest error rate.
* Step 13: Perform the transaction operation CIS to CIS, apply hash function \(\text{hash}=\text{HASH}\) (CIS), and compute the error (ER).  
If any page includes new information, according to [25], it is not necessary to retrain until the consensus model is identified; if the error is less than, the new data do not provide ample information for the model; so, no update or conversion is needed. The same method can also be applied to other methods. Another scenario occurs when a platform leaves a private blockchain [26; 27]. However, we can also neglect when the site exits while changing the model. This is because certain code transitions are just conceptual; the database architecture does not change until the node finishes the software update. Improvements in the model can be continued. The core algorithm for determining the learning order replaces the learning process before the consensus model is established. In Algorithm one, the CM model is applied and the second phase initializes the correct information. Select the all-new user for operation (create the new blockchain network) while the consensus learning method in the algorithm occasionally pauses before the consensus model is checked, continues, and never ends. Smart assets and contracts can be deployed automatically on every private network website. The entire transaction-based process occurs when a patient is admitted or visits a hospital, and the data are generated in diagnostic reports known as electronic health records (EHR). Blockchain technology is implemented and a smart contract is placed based on data availability from an authorized healthcare center; this also applies if the data are structured and stored through a 5G network-based [28] data storage scheme. Furthermore, in Figure 5, the data are transferred to a highly secure decentralized database, which converts the data into encrypted data that can be shared among different healthcare centers.