An IoMT-Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique.

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result.

Investigating the awareness level about diabetes mellitus and associated factors in Tarlai (rural Islamabad).

OBJECTIVES: To determine the baseline level of awareness amongst a rural community about Diabetes Mellitus (irrespective of type 1 or type 2), its risk factors and complications. METHODS: This was a cross-sectional survey conducted at the community of Tarlai, Islamabad, Pakistan, in January of 2008. A structured questionnaire was used and 300 adults (age > or = 18 years) were assessed on their knowledge regarding awareness of Diabetes Mellitus, its risk factors and complications. All data collected was entered into SPSS version 10.0. The data was re-validated and analyzed. RESULTS: Out of the three hundred adults subjected to the survey, only 129 (43%) adults had any awareness of Diabetes Mellitus. Adults with no regular, scheduled exercise were 221 (73.7%) and 256 (85.3%) did not have healthy eating habits. Awareness of risk factors was present in 42 (14%) while awareness of the complications associated with the disease was 65 (22%). Adults which reported as never going for regular checkups to any clinic or hospital were 232 (77%). Family history of diabetes mellitus was statistically significantly associated with awareness about diabetes mellitus (65% vs 32%, p < 0.001), people who were in contact regularly with health care providers were more aware about diabetes and the associated risk factors than those who were not (71% vs 35%, p < 0.001). Sex was not associated (p = 0.28) with awareness about diabetes mellitus, nor was the educational status (p = 0.46). CONCLUSIONS: Majority of adults were unaware of Diabetes Mellitus itself and associated risk factors. Raising public awareness of the disease through outreach programmes and mass media should be planned and implemented.