## 4 SSHies: An Era of Research
### AI and Blockchain-Based Techniques in HIE Systems: A Background
Decision tree (dt) and random forest (rf) are the best ML models, with an accuracy value of 0.889. For anomaly detection, soft classification is better than hard, with Bernoulli naive Bayes with non-normalized data showing the best performance. The study concludes by proposing a comparative study between different ML methods in the security of HER, dividing the study into normal and anomaly data, and proposing soft and hard classification for comparison. The authors in [113] proposed a method to detect anomalies in blockchain electronics transactions using machine learning. They used two algorithms: the OCSVM algorithm to detect outliers and the k-means algorithm to group similar outliers with the same type of anomalies. The method involved analyzing 90514 values in 33 time series, extracting features such as blockchain size, difficulty changes over time, hash rate, transaction volume, median transaction confirmation time, total number of unique Bitcoin transactions per day, average block size. The data set for the experiment was Bitcoin transaction data. The experiment demonstrated that both methods generated good accuracy in detecting anomalies in bitcoin electronic transactions. The study [114] aimed to classify blockchain transactions into risky and non-risky using ensemble learning with or without feature selection. The technique involves extracting features from a data set, labeling them, and applying ML approaches using an ensemble of classifiers. The ensemble learning combines k-nearest neighbor (KNN) and Bayesian approaches for parametric and Bayesian networks, decision trees, and logistic Bayesian approaches for non-parametric. The study demonstrates the effectiveness of ensemble learning in detecting risky blocks in blockchain transactions. The authors of [115] discussed the integration of blockchain and AI in healthcare, highlighting the potential for creating reliable AI models through blockchain. Blockchain, an open network for sharing and authorizing information, is used in various healthcare use cases such as supply chain transparency, smart contracts, patient-centric electronic health records, medical staff credential verification, IoT security, and tracking clinical trials and pharmaceuticals. Artificial intelligence in e-health includes virtual advisors, advanced data analysis, personal life coaches, bots, security and privacy metrics, and AI-based tools like Framingham risk score, QRISK3, MELD score, ABCD2 score, Nottingham Prognostic Index, and XNAT. The integration of blockchain and AI in healthcare, biomedical research, data storage, and fighting COVID-19 can improve service efficiency, reduce costs, and democratize healthcare. Blockchain enables the storage of cryptographic records, which AI needs. Healthcare professionals can access blockchain to display patient medical records, while AI uses various algorithms and decision-making capabilities. The study of [116] presented a novel fraud detection methodology for healthcare fraud using sequence mining concepts. The methodology provides a comprehensive understanding of recurring patient visits and extracts visit patterns, raising awareness among doctors and hospital management about possible medical services. It also enables service providers to take preventive actions in case of anomalous behavior. The use of patient time series traces allows for healthcare utilization details. The sequence rule engine is generated based on sequences with different pattern lengths, determining healthcare service utilization. The architecture is divided into two views: case view and logical view. The methodology has been validated using transactional data from a local hospital over the last five years. Table 3 shows the summary of the state-of-the-art techniques that integrates both AI and blockchain into HIE system in terms of publication year, proposed methods, availability of data sets, and obtained results.