Heart disease risk meter

Greek researchers have developed a quick and convenient method to measure our heart attack risk. Doctors can use this system to provide patients with information about their risks as well as tips for some changes in life or medications to minimize the risk of heart attack.

Lifestyle factors such as depression, education, smoking, diet and obesity play a role in the risk of heart disease. However, epidemiologists study the diversity of disease risks in the community, yet have not figured out how to extrapolate from such extensive studies to determine the individual's risk of disease.

Currently, Hara Kostakis of the Piieus TEI Research Center, at Methonis, Greece and colleagues studied the risk factors for heart disease in a large population group.They used data from 1000 patients who participated in CARDIO 2000, who were hospitalized with symptoms of ACS, acute coronary heart disease . They recorded details of body data, family history, physical activity, high blood pressure, high cholesterol, and diabetes. They then compared elephant data with strong showing.

Lifestyle factors such as depression, education, smoking, diet and obesity play a role in the risk of heart disease. (Photo: blog.kir.com)

Instead of using conventional computational analysis methods, researchers use a method of computer science, OLAP. The direct analysis program was developed in the early 1999s and is used in commercial and industrial applications of financial analysis and marketing.

Essentially, OLAP provides a multidimensional dimension of information to find models even in the largest amount of data. In the standard model, sales, prices, customers and other economic evaluation factors are used. Colleagues from Patra University Kostakis used this system to understand the dangers of heart disease.

The research team pointed out that the CARDIO 2000 study found a relationship between some demographics, nutrition, psychology, lifestyle and health risks, but not entirely for epidemiologists and doctor as a method of diagnosing results. This method provides information for their own risk based on the specific situation.

Kostakis and his colleagues added that their method works faster than conventional analysis, revealing hidden risks and relationships, and not making predictions such as the conventional method used to Assess the risk of heart disease.

The researchers concluded: 'Because of the convenience and ease of using this method, a doctor has the advantage of easily identifying high-risk patients by looking at their personal information. they are for this model ' . Since then, learning can provide appropriate medicine methods based on different risk factors. .

Reference:An algorithm computational for risk assessment of developing coronary syndromes, using online analytical process methodology.Int.J. Knowledge Engineering and Soft Data Paradigms, 2009, 1, 85-99