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Revathi, V.
- A Study on Nutritional Status, Behaviour and Life Style Management among Coronary Heart Disease Patients Aged 40-60 Years
Authors
1 Narayana Hrudayala Hospital, Narayana Hrudayalaya, Hyderabad, Telangana, IN
2 Department of Home Science, Sri Padmaavthi Mahila Visvavidyalayam, Tirupati, Andhra Pradesh, IN
3 Department of Nutrition, Spandana Degree College, Vansthalipuram, Hyderabad, Telangana, IN
Source
International Journal of Innovative Research and Development, Vol 5, No 9 (2016), Pagination: 206-232Abstract
Introduction: In India, heart disease effects people of all ages but is most frequent in middle age and most often cause by arthrosclerosis. With urbanisation and economic development, a nutritional transition characterised by improvement in socio economic status and increasingly sedentary life style have been observed which has contributed to the increasing prevalence of coronary heart disease among the adult population.
Objectives: The study was exploratory and aimed at assessing the diet and life style patterns, nutritional status and estimating the prevalence of coronary heart disease risk factors among the patients followed by a nutrition education programme. Strict adherence to all the prescribed dietary modification and life style changes shows significant improvement in biochemical parameters.
Methodology: Patients aged 25-60 years from hospital Narayan Hrudayalaya underwent anthropometric measurements (body mass index) Body fat percentage, protein mass, body fat mass skeletal mass etc.,. A detail interview will be conducted with the help of structured questionnaire. Questionnaire contain four parts, personal details, family history, life style and nutrition questions (frequency questionnaire, 24recall, etc..,)
Conclusion: In patients with hypertension, the combination of diet control and behavioural modification effectively lower the B.P and may be useful in treating and reducing Coronary heart disease risk factors.
- Event Prediction with Dynamic Knowledge Base on Health Care Data
Authors
Source
International Journal of Innovative Research and Development, Vol 3, No 2 (2014), Pagination:Abstract
The association rule mining techniques are used to detect activities from data sets. Event detection refers to an action taken to an activity. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used. Event detection system identifies the new events in uncertainty environment. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. The event derivation system is enhanced to map dynamic rules on uncertain data environment. Rule probability estimation is carried out using the apriori algorithm. The rule derivation process is optimized for domain specific model.