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Lipid profile prediction based on artificial neural networks

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Abstract

Lipid profile usually includes levels of total cholesterol (TCH), low density lipoprotein (LDL), high density lipoprotein (HDL) and triglycerides (TG), all of which require a blood test. Using advances in machine learning and a relationship between lipid profile and obesity, a model that predicts lipid profile without using any laboratory results can be developed and used in clinical diagnosis. The causal relationship between lipid profile and obesity is well known—TCH, LDL and TG show an increase, while HDL is decreased in obese persons. In this paper we are using artificial neural networks (ANN) to estimate the lipid profile values using non-lab electronic health record data and some measures of obesity. The ANN inputs are gender, age, systolic and diastolic blood pressures, and a single or a combination of multiple obesity parameters, which include body mass index, saggital abdominal diameter to height ratio, waist to height ratio and body fat percentage. Study shows that the presented solution is suitable for prediction of TCH (with accuracy 81.89%), LDL (with accuracy 79.29%) and HDL (with accuracy 81.23%), while not suitable for TG prediction (with accuracy 44.48%).

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Acknowledgements

This work was partially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia within the Projects: ON 174026 and III 044006, and by the Provincial Secretariat for Higher Education and Scientific Research of the Autonomous Province of Vojvodina within the Projects: 114-451-2856/2016-02 and 142-451-3557/2017-01.

Funding

This study was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia (ON 174026 and III 044006) and by the Provincial Secretariat for Higher Education and Scientific Research of the Autonomous Province of Vojvodina (114-451-2856/2016-02 and 142-451-3557/2017-01).

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Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Republic of Serbia

    Milan Vrbaški, Rade Doroslovački, Aleksandar Kupusinac & Dragan Ivetić

  2. Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, Novi Sad, 21000, Republic of Serbia

    Edita Stokić

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  1. Milan Vrbaški
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  2. Rade Doroslovački
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  5. Dragan Ivetić
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Corresponding author

Correspondence to Rade Doroslovački.

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Ethical approval

This research was approved by Ethical Committee of the Clinical Centre of Vojvodina, Republic of Serbia (No. 00–20/354). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Appendix

Appendix

See Tables 4, 5, 6, 7, 8, 9, 10 and 11.

Table 4 Prediction of TCH by using input vectors \(\bar{X^5_1}\), \(\bar{X^5_2}\), \(\bar{X^5_3}\) and \(\bar{X^5_4}\)
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Table 5 Prediction of TCH by using input vectors \(\bar{X^6_1}\), \(\bar{X^6_2}\), \(\bar{X^6_3}\), \(\bar{X^6_4}\), \(\bar{X^6_5}\) and \(\bar{X^6_6}\)
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Table 6 Prediction of TG by using input vectors \(\bar{X^5_1}\), \(\bar{X^5_2}\), \(\bar{X^5_3}\) and \(\bar{X^5_4}\)
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Table 7 Prediction of TG by using input vectors \(\bar{X^6_1}\), \(\bar{X^6_2}\), \(\bar{X^6_3}\), \(\bar{X^6_4}\), \(\bar{X^6_5}\) and \(\bar{X^6_6}\)
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Table 8 Prediction of LDL by using input vectors \(\bar{X^5_1}\), \(\bar{X^5_2}\), \(\bar{X^5_3}\) and \(\bar{X^5_4}\)
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Table 9 Prediction of LDL by using input vectors \(\bar{X^6_1}\), \(\bar{X^6_2}\), \(\bar{X^6_3}\), \(\bar{X^6_4}\), \(\bar{X^6_5}\) and \(\bar{X^6_6}\)
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Table 10 Prediction of HDL by using input vectors \(\bar{X^5_1}\), \(\bar{X^5_2}\), \(\bar{X^5_3}\) and \(\bar{X^5_4}\)
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Table 11 Prediction of HDL by using input vectors \(\bar{X^6_1}\), \(\bar{X^6_2}\), \(\bar{X^6_3}\), \(\bar{X^6_4}\), \(\bar{X^6_5}\) and \(\bar{X^6_6}\)
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Vrbaški, M., Doroslovački, R., Kupusinac, A. et al. Lipid profile prediction based on artificial neural networks. J Ambient Intell Human Comput 14, 15523–15533 (2023). https://doi.org/10.1007/s12652-019-01374-3

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