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Health care interoperability unfolds the way for personalized health care services at a reduced cost. Furthermore, a decentralized system holds the promise to prevent compromises such as cyber-attacks due to data breaches. Hence, there is a need for a framework that seamlessly integrates and shares data across the system stakeholders. We propose SEquestered aNd SynergIstic BLockchain Ecosystem (SENSIBLE), a blockchain-powered, knowledge-driven data-sharing framework that gives patients complete control of their medical history and can extract rich information hidden in it using knowledge graphs (KGs). By incorporating both blockchain and KGs, we can provide a platform for secure data sharing among stakeholders by maintaining data privacy and integrity through data authentication and robust data integration. We present a Proof-of-Concept of the SENSIBLE network with Ethereum to share dynamic knowledge across stakeholders. Dynamic knowledge generation on the blockchain provides a two-fold advantage of cooperation and communication amongst the stakeholders in the health care ecosystem. This leads to operational ease through sharing relevant portions of complex information while also ensuring the isolation of sensitive medical data.

Recommender systems assist the consumers of service oriented environment to find out and select the most suitable services from a large number of available ones. Proposed paper is based on Personalized Recommendation System for medical assistance using keyword extraction. User can search doctor"s profiles or hospital names according to doctor and hospital attributes. Natural Language Processing (NLP) is used to process user"s ratings and reviews to compute system ratings. Depending on users rating and reviews, profiles are recommended. Medical-based Personalized Recommendation System computes similarity between given and collected attribute by using top-k query which is used to recommend each doctor profile and hospital name for each attribute in information retrieval. Personalized Recommendation system for medical assistance yields 0.06 satisfactions and 0.02 accuracy.

Electrocardiogram (ECG) is a signal with unique, valuable information about the functional aspects of the heart with respect to time. The automatic analysis of ECG signals is an important application since the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. The ECG is collected using a number of electrodes placed in different positions on the body. Multi-lead ECGs acquired simultaneously helps in better diagnosis of heart diseases. This paper focuses on classification of healthy and Myocardial infarction signals. The identification of acute myocardial infarction with symptoms of Ischemia is critical to delivering appropriate medical care. In this paper decision tree based classifiers are implemented for the classification of ECG signals. The signals were analyzed for 34 normal and 33 myocardial infarction patients in the database PTB from the domain Physionet.org. The classifiers, J48 and Classification and Regression Trees (CART) are compared with respect to accuracy measures. The J48 classifier performs better with the correct classification rate of 98% and 0.9 Kappa statistics.

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