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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Predictive and Descriptive Analysis for Heart Disease Diagnosis

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DOI: http://dx.doi.org/10.15439/2017F219

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 155163 ()

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Abstract. The heart disease describes a range of conditions affecting our heart. It can include blood vessel diseases such as coronary artery disease, heart rhythm problems or and heart defects. In addition, this term is often used for cardiovascular disease, i.e. narrowed or blocked blood vessels leading to a heart attack, chest pain or stroke. In our work, we analyzed three available datasets focused on heart diseases: Heart Disease Database, South African Heart Disease, Z-Alizadeh Sani Dataset and, Cardiac Dataset. For this purpose, we focused on two directions: predictive analysis based on Decision trees, Na\"{\i}ve Bayes, Support Vector Machine and Neural networks; descriptive analysis based on association and decision rules. Our results are plausible, in some cases comparable or better as in other related works.


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