Disease risk prediction is a rising challenge in the medical domain. Researchers have widely used machine learning algorithms to solve this challenge. The k-nearest neighbour (KNN) algorithm is the most frequently used among the wide range of machine learning algorithms. This paper presents a study on different KNN variants (Classic one, Adaptive, Locally adaptive, k-means clustering, Fuzzy, Mutual, Ensemble, Hassanat and Generalised mean distance) and their performance comparison for disease prediction. This study analysed these variants in-depth through implementations and experimentations using eight machine learning benchmark datasets obtained from Kaggle, UCI Machine learning repository and OpenML. The datasets were related to different disease contexts. We considered the performance measures of accuracy, precision and recall for comparative analysis. The average accuracy values of these variants ranged from 64.22% to 83.62%. The Hassanaat KNN showed the highest average accuracy (83.62%), followed by the ensemble approach KNN (82.34%). A relative performance index is also proposed based on each performance measure to assess each variant and compare the results. This study identified Hassanat KNN as the best performing variant based on the accuracy-based version of this index, followed by the ensemble approach KNN. This study also provided a relative comparison among KNN variants based on precision and recall measures. Finally, this paper summarises which KNN variant is the most promising candidate to follow under the consideration of three performance measures (accuracy, precision and recall) for disease prediction. Healthcare researchers and stakeholders could use the findings of this study to select the appropriate KNN variant for predictive disease risk analytics.

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Artificial Intelligence (AI) is the biggest emerging movement and promise in today’s technology world. Artificial Intelligence (AI) in contrast to Natural (human or animal) Intelligence, is intelligence demonstrated by machines. AI is also called Machine Intelligence, aims to mimic human intelligence by being able to obtain and apply knowledge and skills.  It promises substantial involvements, vast changes, modernizations, and integration with and within people’s ongoing life. It makes the world more demanding and helps to take the prompt and appropriate decisions with real time. This paper provides a main analysis of health industry and health care system in Australian Healthcare that are relevant to the consequences formed by Artificial Intelligence (AI). This paper primarily has used secondary research analysis method to provide a wide-ranging investigation of the positive and negative consequences of health issues relevant to Artificial Intelligence (AI), the architects of those consequences and those overstated by the consequences. The secondary resources are subject to journal articles, reports, academic conference proceedings, media articles, corporation-based documents, blogs and other appropriate information. The study found that Artificial Intelligence (AI) provides useful insights in Australian Healthcare system. It is steadily reducing the cost of Australian Healthcare system and improving patients’ overall outcome in Australian Healthcare. Artificial Intelligence (AI) not only can improve the affairs between public and health enterprises but also make the life better by increasing efficiency and modernization. However, beyond the technology maturity, there are still many challenges to overcome before Australian Healthcare can fully leverage the potential of AI in health care - Ethics being one of the most critical.   Keywords: Artificial Intelligence (AI), Health Industry, Health Care System, Australian Healthcare;

This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.