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Multivariable prediction model of complications derived from diabetes mellitus using machine learning on scarce highly unbalanced data

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International Journal of Diabetes in Developing Countries Aims and scope Submit manuscript

Abstract

Background

Diabetes mellitus (DM) increases the risk complications in addition to mortality. Quantifying the risk of complications using artificial intelligence could be a way to design comprehensive patient healthcare programs.

Objective

Predicting the probability of macro and microvascular complications in patients with DM through Machine Learning.

Methods

Retrospective cohort study. Based on an outpatient follow-up program for diabetic patients, 64,081 records and 287 variables were identified, with highly unbalanced data. Predictive models for chronic kidney disease (CKD), lower extremity amputation (LEA), coronary heart disease (CHD), and early mortality (MOR) were developed. An exhaustive computational method was conducted to find the best combination between machine learning (ML) algorithms and sampling method.

Results

The best model was determined by assessing its performance through the heuristics obtained from a comprehensive analysis of the accuracy and F1 values for ML, sampling, and dataset. Regarding each complication, 99.9% accuracy was obtained for LEA, 94.3% for CHD, 97.4% for MOR, and 98.8% for CKD. F1 was assessed to identify false positives, with 84.5% for CKD, 63.6% for MOR, 46.2% for LEA, and 44.8% for CHD.

Conclusions

This ML model can be applied to predict CHD, CKD, and MOR. The success of ML predictions lies in the clinical definition of initial variables and their simplification for obtaining variables based on which the algorithms can identify patients that are likely to develop a complication. For clinical application of this system, it is necessary to assess the cross performance of metrics, as found here (accuracy higher 95% and F1-Score higher than 80%).

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Data Availability

Data is available through the corresponding author upon justified request.

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Acknowledgment

The authors want to thank the funding institutions: MINCIENCIAS, EPS SANITAS Colombia, as well as University of Santander (UDES) and University Foundation SANITAS for all the support in this process.

Funding

This study was funded by Ministry of Science Technology and Innovation of the Republic of Colombia (a.k.a. MINCIENCIAS) from call 811 of 2018, under code C160I000000011758-19, contract number 433 of 2019, and contingent recovery number 80740-433-2019.

Author information

Authors and Affiliations

  1. Fundación Universitaria Sanitas, Bogotá D.C., Colombia

    Claudia C. Colmenares-Mejía & Andrés Cely

  2. Univerisdad de Santander, Campus Lagos del Cacique, Bucaramanga, Santander, Colombia

    Juan C. Rincón-Acuña

  3. Data Scientist. Corporate Data Management, Keralty, Bogotá D.C., Colombia

    Juan C. Rincón-Acuña

  4. Universidad Nacional de Colombia, Bogotá D.C., Colombia

    Andrés Cely

  5. Preventive Medicine Service, University Hospital of Torrejón, Torrejón de Ardoz, Spain

    Abel E. González-Vélez

  6. Dirección Gestión del Conocimiento, EPS Sanitas, Bogotá D.C, Colombia

    Andrea Castillo

  7. Instituto de Gerencia y Gestión Sanitaria, Fundación Universitaria Sanitas, Bogotá D.C., Colombia

    Jossie Murcia

  8. Research group INPAC, Fundación Universitaria Sanitas, Bogotá D.C., Colombia

    Mario A. Isaza-Ruget

Authors
  1. Claudia C. Colmenares-Mejía
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  2. Juan C. Rincón-Acuña
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  3. Andrés Cely
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  4. Abel E. González-Vélez
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Correspondence to Claudia C. Colmenares-Mejía.

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Colmenares-Mejía, C.C., Rincón-Acuña, J.C., Cely, A. et al. Multivariable prediction model of complications derived from diabetes mellitus using machine learning on scarce highly unbalanced data. Int J Diabetes Dev Ctries (2023). https://doi.org/10.1007/s13410-023-01264-7

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  • DOI: https://doi.org/10.1007/s13410-023-01264-7

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