Public health care systems routinely collect health-related data from the population. This data can be analyzed using data mining techniques to find novel, interesting patterns, which could help formulate effective public health policies and interventions. The occurrence of chronic illness is rare in the population and the effect of this class imbalance, on the performance of various classifiers was studied. The objective of this work is to identify the best classifiers for class imbalanced health datasets through a cost-based comparison of classifier performance. The popular, opensource data mining tool WEKA, was used to build a variety of core classifiers as well as classifier ensembles, to evaluate the classifiers" performance. The unequal misclassification costs were represented in a cost matrix, and cost-benefit analysis was also performed. In another experiment, various sampling methods such as under-sampling, over-sampling, and SMOTE was performed to balance the class distribution in the dataset, and the costs were compared. The Bayesian classifiers performed well with a high recall, low number of false negatives and were not affected by the class imbalance. Results confirm that total cost of Bayesian classifiers can be further reduced using cost-sensitive learning methods. Classifiers built using the random under-sampled dataset showed a dramatic drop in costs and high classification accuracy.

The Insurance industry participates in various processes that are characterized by data exchange, which is modified or updated by many parties. Hence, the insurance industry can benefit from the adoption of blockchain technology. However, there is a lack of understanding of the technology, the legal implications and the issues in implementing the technology. This paper aims at finding potential opportunities for the insurance sector on the implementation of blockchain technology. It also discusses issues and concerns for insurance companies wanting to adopt block chain technologies. A search was carried out for relevant electronic bibliographic databases (searched by means of keywords), articles published in scientific journals, websites of consultancy firms and blockchain developers, and reference lists of relevant review articles. Articles were screened and eligibility was based on participants, procedures, interventions comparisons, outcomes (PICO) model and criteria for PRISMA (Preferred Reporting Items for Systematic Reviews). A total of 23 papers were finalized after scrutiny for this study whereby the results disclose that blockchain, as a single source of reality, has the potential to improve productivity and mitigate the complexity of the insurance processes. Examples of real-world applications and insurance use cases are presented to demonstrate the strengths & capabilities of the technology. This study also considers the present-day issues, risks and concerns in the implementation of the blockchain technology. Finally, the challenges and obstacles in the application of Blockchain technology in the Insurance Sector is highlighted and presented.

No abstract

No abstract

Patients go to multiple healthcare providers for treatment, and their health data is generally distributed among providers. The distributed health data and the decentralized health care system structure make it ideal for blockchain-based health information systems. The authors consider the referral use case; for instance, a patient goes to his primary health Centre (PHC) for treatment and is referred to a hospital. Authentication is usually done using certificates or key cryptography, which could become cumbersome when multiple parties are involved in a healthcare interaction. The security requirements were defined, and a novel multi-party, mutual patient identity authentication scheme called "Distributed Dynamic Mutual Identity Authentication (DDMIA)" was proposed for the referral use case in a blockchainbased e-health network. The DDMIA enables the PHC to authenticate the patient to the referred hospital. The DDMIA scheme was designed using Elliptic Curve Cryptography. It was proven to be secure by assuming the hardness of the elliptic curve discrete log problem (ECDLP) and Elliptic curve computational Diffie-Hellman problem (ECDH) using CK-Model. The formal security analysis using BAN logic proved that the sessions are secure after authentication. The DDMIA scheme was simulated in the AVISPA tool and proven safe against all active attacks. DDMIA scheme was simulated in the AVISPA tool and proven safe against all active attacks. The scheme allows a patient to be authenticated by multiple parties without registering with all parties. It eliminates the need for multiple registration centers as well as digital certificates. Hence, the DDMIA scheme can be implemented for similar multiparty authentication requirements in blockchain-based networks.

No abstract

No abstract

No abstract