Blockchain and Artificial Intelligence-based Solutions for Healthcare Management: Liver Disease Detection as a Case Study.

The issue of privacy in clinical data restricts data sharing among various organizations because of legal and ethical concerns. Every medical organization (hospital, research center, testing lab, etc.) needs to protect personal and medical data privacy and confidentiality while also sharing data with efficient and accurate learning models for various diseases. This paper addresses a method that combines machine learning approaches on blockchain network for classification of patients with liver disease or not, while maintaining the privacy and enabling data sharing. The contribution of this paper is five-folds: (i) To deal with the liver disease dataset, data preprocessing phases are utilized (i.e., missing data treatment, data normalization, and data transformation). (ii) For feature reduction, two feature selection models are used: correlation feature selection (CFS) and wrapper metaheuristic (PSO+KNN). For training the proposed global model, the selected features (10, 4, 3) from the three models (all features, CFS, PSO+KNN) are distributed to the shared blockchain network. (iii) To detect liver disease in the preprocessed dataset, three machine learning methods are proposed: random forest, decision tree (J48), and Naive Bayes. (iv) The distributed network (blockchain) will publish the results of the liver disease diagnosis on the network. (v) The smart contract shares the data and diagnosis between the medical organizations for future processing. An empirical study is executed to validate the relevance of our suggested framework for early diagnosis of liver disease using blockchain and machine learning models. The experimental results showed that feature selection using the wrapper metaheuristic (PSO+KNN) with a random forest model achieves the best performance with the minor features for detecting liver disease with an accuracy of 99.9 compared to 99.2, 89.2 for Naive Bayes, and J48, respectively.