Fuzzy Near Compactness Based Personalized Recommendation for Preventing Patients with Type 2 Diabetes Mellitus and Hypertension from Cardiovascular Complication

Undeniably, preventing cardiovascular complication for patients with type 2 diabetes mellitus and hypertension is a challenge in the healthcare area. However, the prevention of these patients from a cardiovascular complication does not obtain much attention despite the rate of morbidity and mortality with cardiovascular complication increasing every year. To prevent each individual patient from not getting worse in health status, personalized recommendation is revealed as the most effective motivation in self-healthcare management with optimal process. Consequently, this paper proposes a personalized recommendation using the intelligent supporting system to prevent the patients with type 2 diabetes mellitus and hypertension from a cardiovascular complication, whereas the appropriated activities of daily living will be recommended as a suitable lifestyle. This recommendation system is modeled with fuzzy near compactness to measure the similarity between the patient’s conditions and practice plans in order to retrieve the most optimal practice programme which is obtained as a personalized plan for each patient. To conduct this model, this paper acquired the clinical data, lifestyle data, and trends of health status data from the patient history. The recommendation by this proposed model was suggested from the fuzzy near compactness algorithm which was constructed from the knowledge and experience of ten medical experts. The experimental results, on 744 datasets based on the total of checking up of 162 patients, show that the proposed recommendation model can achieve 96.63% accuracy when compared with the diagnostic results made by other five medical experts.