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Abstract-In this paper, an intelligent recommender system is developed, which uses an innovative time series prediction algorithm to provide recommendations to heart disease patients in the tele-health environment. Based on analytics of each patient's medical tests in records, the system provides the patient with decision support for necessity of medical tests. The experimental results show that the proposed system yields satisfactory accuracy in recommendations. The system also offers a promising way for saving the workload for patients and healthcare practitioners in conducting daily medical tests. The research will help reduce the workload and cost in healthcare and help the healthcare industry transform from the traditional scenario to more a personalized paradigm in a tele-health environment.

Abstract. The use of intelligent technologies for providing useful recommendations to patients suffering chronic diseases may play a positive role in improving the general life quality of patients and help reduce the workload and cost involved in their daily healthcare. The objective of this study is to develop an intelligent recommender system based on predictive analysis for advising patients in the telehealth environment concerning whether they need to take the body test one day in advance by analyzing medical measurements of a patient for the past k days. The proposed algorithms supporting the recommender system have been validated using a time series telehealth data recorded from heart disease patients which were collected from May to January 2012, from our industry collaborator Tunstall. The experimental results show that the proposed system yields satisfactory recommendation accuracy and offer a promising way for saving the workload for patients to conduct body tests every day. This study highlights the possible usefulness of the computerized analysis of time series telehealth data in providing appropriate recommendations to patients suffering chronic diseases such as heart diseases patients.

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