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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2019: Intelligent Information and Database Systems pp 707–717Cite as

Predicting Cardiovascular Risk Level Based on Biochemical Risk Factor Indicators Using Machine Learning: A Case Study in Indonesia

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11432))

Abstract

Early detection of cardiovascular risk level remains an important issue in healthcare. It is still considered a very important preventive measure of cardiovascular disease as it gives a significant impact to reducing mortality rates and cardiovascular events. Prior to developing a prediction model of cardiovascular risk, identification of dominant predictor variables is very crucial. Some prominent studies have proposed a vast number of predictor variables. Although some predictor variables might be universal in nature, as the premise of this study, some of the variables might be associated with local lifestyle that governs patient behavior. This paper presents a verificative study on previous studies predicting cardiovascular risk level by using Indonesian adult patients’ lab records as the input dataset. In relation to this objective, this study aimed to select dominant biochemical indicators as predictor variables and trained machine learning models as classifier. Finally, this study compared the performance of several prominent classifier models such as: XGBoost, Random Forest, k-NN, Gradient Boosting, Artificial Neural Network (Multilayer Perceptron), Decision Tree, and Ada Boost. The results show that: XGBoost model achieved the best training and testing accuracy (0.965 and 0.964) compared to Random Forest (0.964 and 0.962), 5-NN (0.952 and 0.948), Gradient Boosting (0.948 and 0.940), Artificial Neural Networks (0.945 and 0.933), Decision Tree (0.861 and 0.860) and Ada Boost models (0.748 and 0.718).

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Author information

Authors and Affiliations

  1. Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia

    Yaya Heryadi

  2. Computer Science Program, Binus International, Bina Nusantara University, Jakarta, Indonesia

    Raymond Kosala & Raymond Bahana

  3. School of Information System, Bina Nusantara University, Jakarta, Indonesia

    Indrajani Suteja

Authors
  1. Yaya Heryadi
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  2. Raymond Kosala
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  3. Raymond Bahana
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  4. Indrajani Suteja
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Corresponding author

Correspondence to Yaya Heryadi .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Bina Nusantara University, Jakarta, Indonesia

    Ford Lumban Gaol

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Heryadi, Y., Kosala, R., Bahana, R., Suteja, I. (2019). Predicting Cardiovascular Risk Level Based on Biochemical Risk Factor Indicators Using Machine Learning: A Case Study in Indonesia. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_61

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  • DOI: https://doi.org/10.1007/978-3-030-14802-7_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14801-0

  • Online ISBN: 978-3-030-14802-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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