A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers
In a Data Science project, it is essential to determine the relevance of the data and identify patterns that contribute to decision–making based on domain–specific knowledge. Furthermore, a clear definition of methodologies and creation of documentation to guide a project&#...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10464294/ |
| _version_ | 1797243412710162432 |
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| author | Jaqueline C. S. Carvalho Tales C. Pimenta Alessandra C. P. Silverio Marcos A. Carvalho Joao Paulo C. S. Carvalho |
| author_facet | Jaqueline C. S. Carvalho Tales C. Pimenta Alessandra C. P. Silverio Marcos A. Carvalho Joao Paulo C. S. Carvalho |
| author_sort | Jaqueline C. S. Carvalho |
| collection | DOAJ |
| description | In a Data Science project, it is essential to determine the relevance of the data and identify patterns that contribute to decision–making based on domain–specific knowledge. Furthermore, a clear definition of methodologies and creation of documentation to guide a project’s development from inception to completion are essential elements. This study presents a Data Science model designed to guide the process, covering data collection through training with the aim of facilitating knowledge discovery. Motivated by deficiencies in existing Data Science methodologies, particularly the lack of practical step–by–step guidance on how to prepare data to reach the production phase. Named “Data Refinement Cycle with Supervised Machine Learning (DRC–SML)”, the proposed model was developed based on the emerging needs of a Data Sciense project aimed at assisting healthcare professionals in diagnosing pesticide poisoning among rural workers. The dataset used in this project resulted from scientific research in which 1027 samples were collected, containing data related to toxicity biomarkers and clinical analyses. We achieved an accuracy of 99.61% with only 27 rules for determining the diagnosis. The results optimized healthcare practices and improved quality of life in rural areas. The project outcomes demonstrated the success of the proposed model. |
| first_indexed | 2024-04-24T18:54:43Z |
| format | Article |
| id | doaj.art-0b4818c923e04bbc8f26878c1a08fc42 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:43Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0b4818c923e04bbc8f26878c1a08fc422024-03-26T17:43:59ZengIEEEIEEE Access2169-35362024-01-0112408714088210.1109/ACCESS.2024.337576410464294A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural WorkersJaqueline C. S. Carvalho0https://orcid.org/0000-0002-6485-430XTales C. Pimenta1https://orcid.org/0000-0002-6485-430XAlessandra C. P. Silverio2https://orcid.org/0000-0003-2093-2713Marcos A. Carvalho3https://orcid.org/0000-0002-3546-5815Joao Paulo C. S. Carvalho4https://orcid.org/0009-0000-6447-2013Institute of Systems Engineering and Information Technology, Federal University of Itajubá, Itajubá, BrazilInstitute of Systems Engineering and Information Technology, Federal University of Itajubá, Itajubá, BrazilDepartment of Computer Science, José do Rosário Vellano University, Alfenas, BrazilInstitute of Systems Engineering and Information Technology, Federal University of Itajubá, Itajubá, BrazilMathematics and Natural Sciences Division, Brescia University, Owensboro, KY, USAIn a Data Science project, it is essential to determine the relevance of the data and identify patterns that contribute to decision–making based on domain–specific knowledge. Furthermore, a clear definition of methodologies and creation of documentation to guide a project’s development from inception to completion are essential elements. This study presents a Data Science model designed to guide the process, covering data collection through training with the aim of facilitating knowledge discovery. Motivated by deficiencies in existing Data Science methodologies, particularly the lack of practical step–by–step guidance on how to prepare data to reach the production phase. Named “Data Refinement Cycle with Supervised Machine Learning (DRC–SML)”, the proposed model was developed based on the emerging needs of a Data Sciense project aimed at assisting healthcare professionals in diagnosing pesticide poisoning among rural workers. The dataset used in this project resulted from scientific research in which 1027 samples were collected, containing data related to toxicity biomarkers and clinical analyses. We achieved an accuracy of 99.61% with only 27 rules for determining the diagnosis. The results optimized healthcare practices and improved quality of life in rural areas. The project outcomes demonstrated the success of the proposed model.https://ieeexplore.ieee.org/document/10464294/Data sciencedecision support systemmachine learningpesticide poisoning diagnosis |
| spellingShingle | Jaqueline C. S. Carvalho Tales C. Pimenta Alessandra C. P. Silverio Marcos A. Carvalho Joao Paulo C. S. Carvalho A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers IEEE Access Data science decision support system machine learning pesticide poisoning diagnosis |
| title | A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers |
| title_full | A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers |
| title_fullStr | A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers |
| title_full_unstemmed | A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers |
| title_short | A New Data Science Model With Supervised Learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers |
| title_sort | new data science model with supervised learning and its application on pesticide poisoning diagnosis in rural workers |
| topic | Data science decision support system machine learning pesticide poisoning diagnosis |
| url | https://ieeexplore.ieee.org/document/10464294/ |
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