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Risk Factors In Adolescent On Arteriosclerosis: An Analysis Using Data Mining And Docking Techniques

Aims: Data related to arteriosclerosis has been collected from different hospitals from Visakhapatnam, India. Data has to been analyzed for future experimentations so that mechanism of metabolites on aging diseases such as arteriosclerosis can be cured. 
Methods: Data related to arteriosclerosis has been collected from different hospitals in Visakhapatnam during January and February 2011. Analysis of this data has been done using machine learning approaches. Docking studies related to prescribed drugs has been conducted using docking software. 
Results: The results showed that the arteriosclerosis occurs mostly to the old people (60-69 years). The data analyzed has been done through data mining techniques which showed 92.9% accuracy using BayesNet. From the data collected, the drugs which are being used are docked with a diseased protein which causes atherosclerosis, showed Betaloc can be an anti-arteriosclerotic effective drug. Arteriosclerosis is a metabolic syndrome and genitical disease, occurring more in the persons who prefer tea rather than coffee. Eighteen percent of the persons are predicted with family background. 
Conclusion: The present results showed that Age, height, gender, weight, family history, tea, coffee, number of times tea/coffee consumption, blood test, cholesterol, diabetes, obesity, drinking and smoking are the factors responsible for arteriosclerosis based on data analysis and machine learning approaches. Clinical research has to be conducted further in this direction.