We propose a method to search rare cardiovascular disease symptom rules from historical health examination records according to its hazard ratio utility and further detect the disease given new medical record data. Further, we aim to assist both medical experts and patients by alerting the current symptoms and preparing the early treatments. In general, the proposed method first deals with the uncertainty of age and other continuous features using a fuzzy set. Next, we define the hazard ratio utility of each item set to assist the mining process. Based on the utility, we discover the rare cardiovascular disease patterns employing High Utility Rare Itemset Mining. At last, we add a prediction step to check the given health record data whether diagnosed cardiovascular. Subsequently, we can obtain rare symptoms of cardiovascular disease, which are later applied to detect the new related record data. The rare symptoms that are confirmed by their utility risk for cardiovascular disease can assist the medical experts' decision better than the common symptoms as it is often hard to be recognized at a glance. The proposed method evaluated on a public cardiovascular dataset. The experimental results showed that the generated rare cardiovascular disease patterns successfully applied to detect the cardiovascular given the symptoms data.

Rare diseases affect less than one out of every 2.000 people, which means they are much less visible in the general population than common diseases. It has several negative consequences for both patients and healthcare systems. Because of this negative effect, healthcare programs and policies must be well-structured. Nonetheless, countries' efforts in this area should be assessed to ensure effectiveness. The aim of this study is to assess the policy performance of 18 OECD countries in terms of rare diseases using health policy indicators.

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With Regards
Sofia
Journal Co-ordinator
Journal of Rare Disorders: Diagnosis & Therapy