Framework modeling for healthcare system based on machine learning and blockchain

Health is a basic need of people and everybody needs good and less costly health facilities. In the current era, there is a great advancement of technology, such system should be provided that ensures easy access and less costly health to everybody. There are a large of research in the medical field, like cure of cancer and uncured diseases, but health cost is still an issue for everybody despite of the availability of much information. The proposed system is developing application can provide real-time information to doctors and health providers about patients, as well as considering the security aspect in the transmission and authentication of the information. This thesis contains two parts: The first part: Provides a better decision support to increase the quality of health and care (using Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and hybrid between Artificial Neural Network and Deep Neural Network), different learning models are trained, without modifying the dataset properties, while their decisions were considered as input to the logical consensus paradigm. The suggested approach abstracts the decision made by different learning models, passing those models behavior to a binary logical decision layer, which produced the results. The training samples, the decision abstraction layer and the final decision were used to update the overall logic of the proposed system. During the training phase, the update logical decision module will keep updating its parameters. After all, the proposed paradigm of combining different learning approaches with a simple binary circuitry achieved almost 100% accuracy. The second part: The next generation blockchain technology can help in reducing the cost of transactions in various government schemes. Healthcare is an industry that requires real-time up gradation and updating in order to meet the intended need for the dispatch of quick and efficient healthcare. A key generator based on logistic map theory can be applied. The key generator is not meant to be regenerated, which means that it is not possible to regenerate the same key. Therefore, the authentication is strengthened.