An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: <i>Algorithms, Methods, and Predictions</i>

Abdul Joseph Fofanah; Tesyon Korjo Hwase

doi:doi:10.11648/j.ajhr.20221006.14

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An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions

Abdul Joseph Fofanah, Tesyon Korjo Hwase

Published in American Journal of Health Research (Volume 10, Issue 6)

Received: 21 October 2022 Accepted: 12 November 2022 Published: 27 December 2022

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Abstract

In this paper we present a cardiovascular diseases prediction which is referred to as heart diseases. A detail review and application of genetic algorithms in healthcare systems including machine learning algorithms were evaluated. Areas in health systems reviewed and, in this research, includes radiology, oncology, cardiology, obstetrics and gynaecology, surgery, and infectious diseases. We conducted a healthcare management with recent reviewed papers and the application of GA in various health systems using its key parameter evaluation metrics; genetic operator, mutation operators, real coded GA, pareto-based multi-objective genetic algorithm and parallel genetic algorithms. The authors also proposed an architecture of a hybrid genetic algorithm and machine learning techniques implemented in MATLAB setting. One of the leading causes of morbidity and mortality in the global population, cardiovascular disease is characterized by restricted or blocked blood vessels that can cause heart attacks, angina, strokes, and other heart failures such muscle, valve, or rhythm problems. According to our analysis and findings, between 85 and 89 percent of people over the age of 40 were significantly affected by cardiovascular diseases. This result is crucial in light of the 2014–2016 Ebola outbreak in West Africa and the ongoing COVID-19 pandemic, both of which disproportionately affected the elderly population. Our findings also suggest that the algorithm gets more complicated and performs better the higher the generation. To forecast the results from the available data, however, and to compare the probability computation with the dataset for cardiovascular disorders, GA and ML techniques are helpful.

Published in	American Journal of Health Research (Volume 10, Issue 6)
DOI	10.11648/j.ajhr.20221006.14
Page(s)	225-256
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

Cardiovascular Diseases, Genetic Algorithms, Machine Learning Algorithms, Genetic Programming, Computational Intelligence

References

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Abdul Joseph Fofanah, Tesyon Korjo Hwase. (2022). An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions. American Journal of Health Research, 10(6), 225-256. https://doi.org/10.11648/j.ajhr.20221006.14

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Abdul Joseph Fofanah; Tesyon Korjo Hwase. An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions. Am. J. Health Res. 2022, 10(6), 225-256. doi: 10.11648/j.ajhr.20221006.14

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Abdul Joseph Fofanah, Tesyon Korjo Hwase. An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions. Am J Health Res. 2022;10(6):225-256. doi: 10.11648/j.ajhr.20221006.14

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@article{10.11648/j.ajhr.20221006.14,
  author = {Abdul Joseph Fofanah and Tesyon Korjo Hwase},
  title = {An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions},
  journal = {American Journal of Health Research},
  volume = {10},
  number = {6},
  pages = {225-256},
  doi = {10.11648/j.ajhr.20221006.14},
  url = {https://doi.org/10.11648/j.ajhr.20221006.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajhr.20221006.14},
  abstract = {In this paper we present a cardiovascular diseases prediction which is referred to as heart diseases. A detail review and application of genetic algorithms in healthcare systems including machine learning algorithms were evaluated. Areas in health systems reviewed and, in this research, includes radiology, oncology, cardiology, obstetrics and gynaecology, surgery, and infectious diseases. We conducted a healthcare management with recent reviewed papers and the application of GA in various health systems using its key parameter evaluation metrics; genetic operator, mutation operators, real coded GA, pareto-based multi-objective genetic algorithm and parallel genetic algorithms. The authors also proposed an architecture of a hybrid genetic algorithm and machine learning techniques implemented in MATLAB setting. One of the leading causes of morbidity and mortality in the global population, cardiovascular disease is characterized by restricted or blocked blood vessels that can cause heart attacks, angina, strokes, and other heart failures such muscle, valve, or rhythm problems. According to our analysis and findings, between 85 and 89 percent of people over the age of 40 were significantly affected by cardiovascular diseases. This result is crucial in light of the 2014–2016 Ebola outbreak in West Africa and the ongoing COVID-19 pandemic, both of which disproportionately affected the elderly population. Our findings also suggest that the algorithm gets more complicated and performs better the higher the generation. To forecast the results from the available data, however, and to compare the probability computation with the dataset for cardiovascular disorders, GA and ML techniques are helpful.},
 year = {2022}
}

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TY  - JOUR
T1  - An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions
AU  - Abdul Joseph Fofanah
AU  - Tesyon Korjo Hwase
Y1  - 2022/12/27
PY  - 2022
N1  - https://doi.org/10.11648/j.ajhr.20221006.14
DO  - 10.11648/j.ajhr.20221006.14
T2  - American Journal of Health Research
JF  - American Journal of Health Research
JO  - American Journal of Health Research
SP  - 225
EP  - 256
PB  - Science Publishing Group
SN  - 2330-8796
UR  - https://doi.org/10.11648/j.ajhr.20221006.14
AB  - In this paper we present a cardiovascular diseases prediction which is referred to as heart diseases. A detail review and application of genetic algorithms in healthcare systems including machine learning algorithms were evaluated. Areas in health systems reviewed and, in this research, includes radiology, oncology, cardiology, obstetrics and gynaecology, surgery, and infectious diseases. We conducted a healthcare management with recent reviewed papers and the application of GA in various health systems using its key parameter evaluation metrics; genetic operator, mutation operators, real coded GA, pareto-based multi-objective genetic algorithm and parallel genetic algorithms. The authors also proposed an architecture of a hybrid genetic algorithm and machine learning techniques implemented in MATLAB setting. One of the leading causes of morbidity and mortality in the global population, cardiovascular disease is characterized by restricted or blocked blood vessels that can cause heart attacks, angina, strokes, and other heart failures such muscle, valve, or rhythm problems. According to our analysis and findings, between 85 and 89 percent of people over the age of 40 were significantly affected by cardiovascular diseases. This result is crucial in light of the 2014–2016 Ebola outbreak in West Africa and the ongoing COVID-19 pandemic, both of which disproportionately affected the elderly population. Our findings also suggest that the algorithm gets more complicated and performs better the higher the generation. To forecast the results from the available data, however, and to compare the probability computation with the dataset for cardiovascular disorders, GA and ML techniques are helpful.
VL  - 10
IS  - 6
ER  -

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Author Information

Abdul Joseph Fofanah

Department of Mathematics and Computer Science, Faculty of Environmental Sciences, Milton Margai Technical University, Freetown, Sierra Leone
Tesyon Korjo Hwase

Software Engineer, MTT Consulting Architects and Engineers PLC, Addis Ababa, Ethiopia

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APA Style

Abdul Joseph Fofanah, Tesyon Korjo Hwase. (2022). An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions. American Journal of Health Research, 10(6), 225-256. https://doi.org/10.11648/j.ajhr.20221006.14

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ACS Style

Abdul Joseph Fofanah; Tesyon Korjo Hwase. An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions. Am. J. Health Res. 2022, 10(6), 225-256. doi: 10.11648/j.ajhr.20221006.14

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AMA Style

Abdul Joseph Fofanah, Tesyon Korjo Hwase. An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions. Am J Health Res. 2022;10(6):225-256. doi: 10.11648/j.ajhr.20221006.14

Copy | Download

@article{10.11648/j.ajhr.20221006.14,
  author = {Abdul Joseph Fofanah and Tesyon Korjo Hwase},
  title = {An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions},
  journal = {American Journal of Health Research},
  volume = {10},
  number = {6},
  pages = {225-256},
  doi = {10.11648/j.ajhr.20221006.14},
  url = {https://doi.org/10.11648/j.ajhr.20221006.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajhr.20221006.14},
  abstract = {In this paper we present a cardiovascular diseases prediction which is referred to as heart diseases. A detail review and application of genetic algorithms in healthcare systems including machine learning algorithms were evaluated. Areas in health systems reviewed and, in this research, includes radiology, oncology, cardiology, obstetrics and gynaecology, surgery, and infectious diseases. We conducted a healthcare management with recent reviewed papers and the application of GA in various health systems using its key parameter evaluation metrics; genetic operator, mutation operators, real coded GA, pareto-based multi-objective genetic algorithm and parallel genetic algorithms. The authors also proposed an architecture of a hybrid genetic algorithm and machine learning techniques implemented in MATLAB setting. One of the leading causes of morbidity and mortality in the global population, cardiovascular disease is characterized by restricted or blocked blood vessels that can cause heart attacks, angina, strokes, and other heart failures such muscle, valve, or rhythm problems. According to our analysis and findings, between 85 and 89 percent of people over the age of 40 were significantly affected by cardiovascular diseases. This result is crucial in light of the 2014–2016 Ebola outbreak in West Africa and the ongoing COVID-19 pandemic, both of which disproportionately affected the elderly population. Our findings also suggest that the algorithm gets more complicated and performs better the higher the generation. To forecast the results from the available data, however, and to compare the probability computation with the dataset for cardiovascular disorders, GA and ML techniques are helpful.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - An Intelligence Computation of Genetic Algorithm and Its Application in Healthcare Systems: Algorithms, Methods, and Predictions
AU  - Abdul Joseph Fofanah
AU  - Tesyon Korjo Hwase
Y1  - 2022/12/27
PY  - 2022
N1  - https://doi.org/10.11648/j.ajhr.20221006.14
DO  - 10.11648/j.ajhr.20221006.14
T2  - American Journal of Health Research
JF  - American Journal of Health Research
JO  - American Journal of Health Research
SP  - 225
EP  - 256
PB  - Science Publishing Group
SN  - 2330-8796
UR  - https://doi.org/10.11648/j.ajhr.20221006.14
AB  - In this paper we present a cardiovascular diseases prediction which is referred to as heart diseases. A detail review and application of genetic algorithms in healthcare systems including machine learning algorithms were evaluated. Areas in health systems reviewed and, in this research, includes radiology, oncology, cardiology, obstetrics and gynaecology, surgery, and infectious diseases. We conducted a healthcare management with recent reviewed papers and the application of GA in various health systems using its key parameter evaluation metrics; genetic operator, mutation operators, real coded GA, pareto-based multi-objective genetic algorithm and parallel genetic algorithms. The authors also proposed an architecture of a hybrid genetic algorithm and machine learning techniques implemented in MATLAB setting. One of the leading causes of morbidity and mortality in the global population, cardiovascular disease is characterized by restricted or blocked blood vessels that can cause heart attacks, angina, strokes, and other heart failures such muscle, valve, or rhythm problems. According to our analysis and findings, between 85 and 89 percent of people over the age of 40 were significantly affected by cardiovascular diseases. This result is crucial in light of the 2014–2016 Ebola outbreak in West Africa and the ongoing COVID-19 pandemic, both of which disproportionately affected the elderly population. Our findings also suggest that the algorithm gets more complicated and performs better the higher the generation. To forecast the results from the available data, however, and to compare the probability computation with the dataset for cardiovascular disorders, GA and ML techniques are helpful.
VL  - 10
IS  - 6
ER  -

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