A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery

Dennis Muriithi; Virginia Mwangi

doi:doi:10.11648/j.ajtas.20241303.12

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A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery

Dennis Muriithi^*

, Virginia Mwangi

Published in American Journal of Theoretical and Applied Statistics (Volume 13, Issue 3)

Received: 31 May 2024 Accepted: 27 June 2024 Published: 20 August 2024

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Abstract

The aim of this research was to design a Machine Learning (ML) approaches to predict surgical outcome associated with perioperative risks factors among patients undergoing elective surgery. The research employed descriptive cross-sectional survey and a sample size of 292 patients. Only adult patients undergoing elective surgery were considered. Machine Learning (ML) Algorithm such as Logistic regression, Support vector machine, k-nearest neighbors and random forest were used to provide insights into how different factors such as patient related perioperative risk, procedure related perioperative risk and health system related perioperative risk influence the likelihood of successful surgical outcome. The study found that Random Forest model achieved the highest cross validation accuracy of 100%, which means it correctly classified all data points in the test set. It implies that the random Forest model was the most suitable for classifying surgical outcome among elective surgery patient at Chuka County Referral Hospital. It had a Kappa of 1 indicating a perfect agreement between its predictions and the ground truth in comparison with other algorithms. In addition, Random Forest model achieves a perfect score (1.0) for sensitivity, precision, F1-Score, and balanced accuracy. This suggests that the model is doing extremely well at correctly classifying both positive and negative cases. Availability of main surgical supplies (health system related perioperative risk factors) had the highest score indicating that it was more important factor for the models predictions than other perioperative risk factors. In this study, the Machine Learning analysis identified unknown parameters associated with successful surgical outcome. An application of Machine Learning algorithms as a decision support tool could enable the medical health practitioners to predict the surgical outcome of patients undergoing elective surgery and consequently optimize and personalize clinical management of patient.

Published in	American Journal of Theoretical and Applied Statistics (Volume 13, Issue 3)
DOI	10.11648/j.ajtas.20241303.12
Page(s)	57-64
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Algorithms, Elective Surgery, Surgical Outcome, Perioperative Risks

References

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[2]	Biccard, B. M., Madiba, T. E., Kluyts, H. L., Munlemvo, D. M., Madzimbamuto, F. D., Basenero, A.,... & Arrey, O. (2018). Perioperative patient outcomes in the African Surgical Outcomes Study: a 7-day prospective observational cohort study. The Lancet, 391(10130), 1589-1598.
[3]	Fleisher, L. A., Fleischmann, K. E., Auerbach, A. D., Barnason, S. A., Beckman, J. A., Bozkurt, B.,... & Wijeysundera, D. N. (2015). 2014 ACC/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: executive summary: a report of the American College of Cardiology/American Heart association Task Force on practice guidelines. Journal of nuclear cardiology: official publication of the American Society of Nuclear Cardiology, 22(1), 162-215.
[4]	Gabriel, R. A., Sztain, J. F., Hylton, D. J., Waterman, R. S., & Schmidt, U. (2018). Postoperative mortality and morbidity following non-cardiac surgery in a healthy patient population. Journal of anesthesia, 32(1), 112-119.
[5]	Mazmudar, A., Vitello, D., Chapman, M., Tomlinson, J. S., & Bentrem, D. J. (2017). Gender as a risk factor for adverse intraoperative and postoperative outcomes of elective pancreatectomy. Journal of Surgical Oncology, 115(2), 131-136.
[6]	Meara JG, Leather AJM, Hagander L, Alkire BC, Alonso N, Ameh EA. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet 2015: 386: 569–624. Meersch, M., Schmidt, C., & Zarbock, A. (2017). Perioperative acute kidney injury: an under-recognized problem. Anesthesia & Analgesia, 125(4), 1223-1232.
[7]	Merkow, Ryan P., et al. (2015) ‘Underlying reasons associated with hospital readmission following surgery in the United States.’ JAMA, Vol. 313, no. 5.
[8]	Muriithi. D. K, Kihoro. J and Waititu. A (2012). Ordinal Logistic Regression Versus Multiple Binary Logistic Regression model for predicting student loan allocation. Journal of Agriculture Science and Technology Vol. 14(1), pp 190-203.
[9]	Oosting, R. M., L. Wauben, S. G., Madete, J. K., Groen, R. S., & Dankelman, J. (2020). Availability, procurement, training, usage, maintenance and complications of electrosurgical units and laparoscopic equipment in 12 African countries. BJS Open, 4(2), 326-331. https://doi.org/10.1002/bjs5.50255
[10]	Virginia. M. M, Gitonga.L Nyamu.H & Kainga.S. (2023). Influence of patient related perioperative risks on surgical outcomes among patients undergoing elective surgery at Chuka County Referral Hospital. ijhsr (www.ijhsr.org). Vol. 13(11), pp 109-121. https://doi.org/10.52403/ijhsr.20231114
[11]	World Health Organization (2018). WHO global report on trends in prevalence of tobacco smoking 2000-2025, second edition. Geneva: World Health Organization http://www.who.int/tobacco/publications/surveillance/trends-tobacco-smoking-second-edition
[12]	Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science/SN Computer Science, 2(3). https://link.springer.com/article/10.1007/s42979-021-00592-x

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APA Style

Muriithi, D., Mwangi, V. (2024). A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery. American Journal of Theoretical and Applied Statistics, 13(3), 57-64. https://doi.org/10.11648/j.ajtas.20241303.12

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ACS Style

Muriithi, D.; Mwangi, V. A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery. Am. J. Theor. Appl. Stat. 2024, 13(3), 57-64. doi: 10.11648/j.ajtas.20241303.12

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AMA Style

Muriithi D, Mwangi V. A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery. Am J Theor Appl Stat. 2024;13(3):57-64. doi: 10.11648/j.ajtas.20241303.12

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@article{10.11648/j.ajtas.20241303.12,
  author = {Dennis Muriithi and Virginia Mwangi},
  title = {A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery
},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {13},
  number = {3},
  pages = {57-64},
  doi = {10.11648/j.ajtas.20241303.12},
  url = {https://doi.org/10.11648/j.ajtas.20241303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20241303.12},
  abstract = {The aim of this research was to design a Machine Learning (ML) approaches to predict surgical outcome associated with perioperative risks factors among patients undergoing elective surgery. The research employed descriptive cross-sectional survey and a sample size of 292 patients. Only adult patients undergoing elective surgery were considered. Machine Learning (ML) Algorithm such as Logistic regression, Support vector machine, k-nearest neighbors and random forest were used to provide insights into how different factors such as patient related perioperative risk, procedure related perioperative risk and health system related perioperative risk influence the likelihood of successful surgical outcome. The study found that Random Forest model achieved the highest cross validation accuracy of 100%, which means it correctly classified all data points in the test set. It implies that the random Forest model was the most suitable for classifying surgical outcome among elective surgery patient at Chuka County Referral Hospital. It had a Kappa of 1 indicating a perfect agreement between its predictions and the ground truth in comparison with other algorithms. In addition, Random Forest model achieves a perfect score (1.0) for sensitivity, precision, F1-Score, and balanced accuracy. This suggests that the model is doing extremely well at correctly classifying both positive and negative cases. Availability of main surgical supplies (health system related perioperative risk factors) had the highest score indicating that it was more important factor for the models predictions than other perioperative risk factors. In this study, the Machine Learning analysis identified unknown parameters associated with successful surgical outcome. An application of Machine Learning algorithms as a decision support tool could enable the medical health practitioners to predict the surgical outcome of patients undergoing elective surgery and consequently optimize and personalize clinical management of patient.
},
 year = {2024}
}

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TY  - JOUR
T1  - A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery

AU  - Dennis Muriithi
AU  - Virginia Mwangi
Y1  - 2024/08/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajtas.20241303.12
DO  - 10.11648/j.ajtas.20241303.12
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 57
EP  - 64
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20241303.12
AB  - The aim of this research was to design a Machine Learning (ML) approaches to predict surgical outcome associated with perioperative risks factors among patients undergoing elective surgery. The research employed descriptive cross-sectional survey and a sample size of 292 patients. Only adult patients undergoing elective surgery were considered. Machine Learning (ML) Algorithm such as Logistic regression, Support vector machine, k-nearest neighbors and random forest were used to provide insights into how different factors such as patient related perioperative risk, procedure related perioperative risk and health system related perioperative risk influence the likelihood of successful surgical outcome. The study found that Random Forest model achieved the highest cross validation accuracy of 100%, which means it correctly classified all data points in the test set. It implies that the random Forest model was the most suitable for classifying surgical outcome among elective surgery patient at Chuka County Referral Hospital. It had a Kappa of 1 indicating a perfect agreement between its predictions and the ground truth in comparison with other algorithms. In addition, Random Forest model achieves a perfect score (1.0) for sensitivity, precision, F1-Score, and balanced accuracy. This suggests that the model is doing extremely well at correctly classifying both positive and negative cases. Availability of main surgical supplies (health system related perioperative risk factors) had the highest score indicating that it was more important factor for the models predictions than other perioperative risk factors. In this study, the Machine Learning analysis identified unknown parameters associated with successful surgical outcome. An application of Machine Learning algorithms as a decision support tool could enable the medical health practitioners to predict the surgical outcome of patients undergoing elective surgery and consequently optimize and personalize clinical management of patient.

VL  - 13
IS  - 3
ER  -

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Author Information

Dennis Muriithi

Faculty of Science and Technology, Chuka University, Chuka, Kenya

Contact Email

http://orcid.org/0000-0002-3210-0925
Virginia Mwangi

Nursing Department, Chuka County Referral Hospital, Chuka, Kenya

Contact Email

http://orcid.org/0000-0002-3210-0925

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Plain Text BibTeX RIS

APA Style

Muriithi, D., Mwangi, V. (2024). A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery. American Journal of Theoretical and Applied Statistics, 13(3), 57-64. https://doi.org/10.11648/j.ajtas.20241303.12

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ACS Style

Muriithi, D.; Mwangi, V. A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery. Am. J. Theor. Appl. Stat. 2024, 13(3), 57-64. doi: 10.11648/j.ajtas.20241303.12

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AMA Style

Muriithi D, Mwangi V. A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery. Am J Theor Appl Stat. 2024;13(3):57-64. doi: 10.11648/j.ajtas.20241303.12

Copy | Download

@article{10.11648/j.ajtas.20241303.12,
  author = {Dennis Muriithi and Virginia Mwangi},
  title = {A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery
},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {13},
  number = {3},
  pages = {57-64},
  doi = {10.11648/j.ajtas.20241303.12},
  url = {https://doi.org/10.11648/j.ajtas.20241303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20241303.12},
  abstract = {The aim of this research was to design a Machine Learning (ML) approaches to predict surgical outcome associated with perioperative risks factors among patients undergoing elective surgery. The research employed descriptive cross-sectional survey and a sample size of 292 patients. Only adult patients undergoing elective surgery were considered. Machine Learning (ML) Algorithm such as Logistic regression, Support vector machine, k-nearest neighbors and random forest were used to provide insights into how different factors such as patient related perioperative risk, procedure related perioperative risk and health system related perioperative risk influence the likelihood of successful surgical outcome. The study found that Random Forest model achieved the highest cross validation accuracy of 100%, which means it correctly classified all data points in the test set. It implies that the random Forest model was the most suitable for classifying surgical outcome among elective surgery patient at Chuka County Referral Hospital. It had a Kappa of 1 indicating a perfect agreement between its predictions and the ground truth in comparison with other algorithms. In addition, Random Forest model achieves a perfect score (1.0) for sensitivity, precision, F1-Score, and balanced accuracy. This suggests that the model is doing extremely well at correctly classifying both positive and negative cases. Availability of main surgical supplies (health system related perioperative risk factors) had the highest score indicating that it was more important factor for the models predictions than other perioperative risk factors. In this study, the Machine Learning analysis identified unknown parameters associated with successful surgical outcome. An application of Machine Learning algorithms as a decision support tool could enable the medical health practitioners to predict the surgical outcome of patients undergoing elective surgery and consequently optimize and personalize clinical management of patient.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery

AU  - Dennis Muriithi
AU  - Virginia Mwangi
Y1  - 2024/08/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajtas.20241303.12
DO  - 10.11648/j.ajtas.20241303.12
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 57
EP  - 64
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20241303.12
AB  - The aim of this research was to design a Machine Learning (ML) approaches to predict surgical outcome associated with perioperative risks factors among patients undergoing elective surgery. The research employed descriptive cross-sectional survey and a sample size of 292 patients. Only adult patients undergoing elective surgery were considered. Machine Learning (ML) Algorithm such as Logistic regression, Support vector machine, k-nearest neighbors and random forest were used to provide insights into how different factors such as patient related perioperative risk, procedure related perioperative risk and health system related perioperative risk influence the likelihood of successful surgical outcome. The study found that Random Forest model achieved the highest cross validation accuracy of 100%, which means it correctly classified all data points in the test set. It implies that the random Forest model was the most suitable for classifying surgical outcome among elective surgery patient at Chuka County Referral Hospital. It had a Kappa of 1 indicating a perfect agreement between its predictions and the ground truth in comparison with other algorithms. In addition, Random Forest model achieves a perfect score (1.0) for sensitivity, precision, F1-Score, and balanced accuracy. This suggests that the model is doing extremely well at correctly classifying both positive and negative cases. Availability of main surgical supplies (health system related perioperative risk factors) had the highest score indicating that it was more important factor for the models predictions than other perioperative risk factors. In this study, the Machine Learning analysis identified unknown parameters associated with successful surgical outcome. An application of Machine Learning algorithms as a decision support tool could enable the medical health practitioners to predict the surgical outcome of patients undergoing elective surgery and consequently optimize and personalize clinical management of patient.

VL  - 13
IS  - 3
ER  -

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