Indian researchers have conducted a data mining exercise to find out important risk factors in increasing the chances of an individual having a heart attack.

The authors confirm that the usual suspects high blood cholesterol, intake of alcohol and passive smoking play the most crucial role in `severe,` `moderate` and `mild` cardiac risks, respectively.

Subhagata Chattopadhyay of the Camellia Institute of Engineering in Kolkata used 300 real-world sample patient cases with various levels of cardiac risk – mild, moderate and severe and mined the data based on twelve known predisposing factors: age, gender, alcohol abuse, cholesterol level, smoking (active and passive), physical inactivity, obesity, diabetes, family history, and prior cardiac event.

He then built a risk model that revealed specific risk factors associated with heart attack risk.

Chattopadhyay explained that the essence of this work essentially lies in the introduction of clustering techniques instead of purely statistical modeling, where the latter has its own limitations in `data-model fitting` compared to the former that is more flexible.

He said that the reliability of the data used, should be checked, and this has been done in this work to increase its authenticity. I reviewed several papers on epidemiological research, where I`m yet to see these methodologies, used.

The study has been published in International Journal of Biomedical Engineering and Technology.