Healthc Inform Res.  2016 Jul;22(3):196-205. 10.4258/hir.2016.22.3.196.

Detection of Cardiovascular Disease Risk's Level for Adults Using Naive Bayes Classifier

• Miranda E
• Irwansyah
• Amelga AY
• Maribondang MM
• Salim M

• KMID: 2357398
• DOI: http://doi.org/10.4258/hir.2016.22.3.196

Abstract

OBJECTIVES
The number of deaths caused by cardiovascular disease and stroke is predicted to reach 23.3 million in 2030. As a contribution to support prevention of this phenomenon, this paper proposes a mining model using a naïve Bayes classifier that could detect cardiovascular disease and identify its risk level for adults.
METHODS
The process of designing the method began by identifying the knowledge related to the cardiovascular disease profile and the level of cardiovascular disease risk factors for adults based on the medical record, and designing a mining technique model using a naïve Bayes classifier. Evaluation of this research employed two methods: accuracy, sensitivity, and specificity calculation as well as an evaluation session with cardiologists and internists. The characteristics of cardiovascular disease are identified by its primary risk factors. Those factors are diabetes mellitus, the level of lipids in the blood, coronary artery function, and kidney function. Class labels were assigned according to the values of these factors: risk level 1, risk level 2 and risk level 3.
RESULTS
The evaluation of the classifier performance (accuracy, sensitivity, and specificity) in this research showed that the proposed model predicted the class label of tuples correctly (above 80%). More than eighty percent of respondents (including cardiologists and internists) who participated in the evaluation session agree till strongly agreed that this research followed medical procedures and that the result can support medical analysis related to cardiovascular disease.
CONCLUSIONS
The research showed that the proposed model achieves good performance for risk level detection of cardiovascular disease.

Keyword

Data Mining; Cardiovascular Diagnostic Techniques; Cardiovascular Diseases; Classification; Bayes Theorem

MeSH Terms

Adult*
Bayes Theorem
Bays*
Cardiovascular Diseases*
Classification
Coronary Vessels
Data Mining
Diabetes Mellitus
Diagnostic Techniques, Cardiovascular
Humans
Kidney
Medical Records
Methods
Mining
Risk Factors
Sensitivity and Specificity
Stroke
Surveys and Questionnaires

Figure

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  Figure 1 Naïve Bayes model for cardiovascular disease risk's level detection.

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  Figure 2 Steps of research study.

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  Figure 3 Risk level profile for each predictor attribute.

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