A PROBABILISTIC MODEL TO DETERMINE POTENTIAL CARDIOVASCULAR DISEASES GIVEN INDIVIDUAL LIFESTYLES

Amna Shifia Nisafani, Arif Wibisono, Adi Cipta Airlangga

Abstract


Heart diseases introduces a great number of fatalities worldwide. The vast majority of heart diseases are due to unhealthy lifestyle. Unfortunately, these lifestyles are widely unknown until the disease appears. This paper aims at developing a probabilistic model that can help individuals to early detect what type of cardiovascular disease that an individual may have if he/she maintain his/her current lifestyle. We identify factors that cause cardiovascular diseases as well as their intertwined relationships by interviewing cardiovascular experts. Subsequently, we construct Bayesian Network (BN) model based on these factors, and conduct sensitivity analysis. From our study, we obtain 14 lifestyles of cardiac and their relationships. Furthermore, there are 15 nodes of BN model to predicts cardiovascular diseases. In addition, based on our sensitivity analysis, we figure that Congenital Diseases, Type of Exercise, Body Mass Index, and Age are the most important factors contributing to cardiovascular diseases.

Full Text:

PDF

References


“Statistical Fact Sheet 2016 Update,” 2016.

N. Townsend, J. Williams, P. Bhatnagar, K. Wickramasinghe, and M. Rayner, CARDIOVASCULAR DISEASE STATISTICS 2014. 2014.

“Heart Disease and Stroke Statistics 2017: At-a-Glance,” 2017.

“Screening for cardiovascular disease and risk factors,” 2011.

A. O. Odegaard, W.-P. Koh, M. D. Gross, J.-M. Yuan, and M. A. Pereira, “Combined Lifestyle Factors and Cardiovascular Disease Mortality in Chinese Men and Women The Singapore Chinese Health Study,” Circulation, pp. 2847–2854, 2011.

P. Mullie and P. Clarys, “Association between Cardiovascular Disease Risk Factor Knowledge and Lifestyle,” Food Nutr. Sci., vol. 2, pp. 1048–1053, 2011.

“Reducing risk in heart disease: an expert guide to clinical practice for secondary prevention of coronary heart disease,” 2012.

J. Lv et al., “Adherence to Healthy Lifestyle and Cardiovascular Diseases in the Chinese Population,” J. Am. Coll. Cardiol., vol. 69, pp. 1116–1125, 2017.

A. Methaila, P. Kansal, H. Arya, and P. Kumar, “Early heart disease prediction using data mining techniques,” Comput. Sci. Inf. Technol., pp. 53–59, 2014.

J. Vijayashree, N. Ch, and Srimannarayanaiyengar, “Heart Disease Prediction System Using Data Mining and Hybrid Intelligent Techniques: A Review,” Int. J. Bio-Science Bio-Technology, vol. 8, no. 4, pp. 139–148, 2016.


Refbacks

  • There are currently no refbacks.


FAKULTAS SAINS DAN TEKNOLOGI
UIN SUSKA RIAU

Kampus Raja Ali Haji
Gedung Fakultas Sains & Teknologi UIN Suska Riau
Jl.H.R.Soebrantas No.155 KM 18 Simpang Baru Panam, Pekanbaru 28293
Email: sntiki@uin-suska.ac.id