Browsing by Author "Suntasig Jaen, Micaela Nicole"

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    “Implementación de un sistema predictivo de enfermedades mediante el análisis exámenes médicos con la utilización de inteligencia artificial para el Laboratorio Salud Familiar del cantón La Maná”
    (Ecuador : La Maná : Universidad Técnica de Cotopaxi (UTC), 2024-08) Suntasig Jaen, Micaela Nicole; Osorio Muñoz, Letty Fernanda; Cajas, Jaime Mesías
    The general objective of this research was to develop a disease-predictive system through the analysis of medical exams using artificial intelligence to improve diagnostic accuracy and efficiency in medical care at the “Salud Familiar” Laboratory in La Maná. The specific objectives included reviewing bibliographic sources to establish a solid theoretical foundation, designing the system using the agile SCRUM methodology and advanced technological tools such as Python, Django, SQLite3, and Scikit-learn, and conducting extensive testing to evaluate the system's accuracy, efficiency, and reliability. The research conclusions were favorable. The bibliographic review provided a robust theoretical foundation, which was fundamental for the successful implementation of the system. The system design was carried out in a structured and efficient manner, ensuring flexibility and adaptability for future improvements. Extensive testing confirmed that the system is highly accurate, efficient, and reliable, significantly enhancing the laboratory's ability to offer early and precise diagnoses. The system's impacts are positive. Technically, it represents a significant advancement by integrating cutting-edge technologies into clinical data management. Socially, it improves the quality of medical care, directly benefiting the community of La Maná. Environmentally, it optimizes resource use and reduces medical waste and paper consumption. Economically, it reduces costs associated with unnecessary tests and incorrect treatments, benefiting both the laboratory and patients. It is recommended to continuously maintain and update the system, train the laboratory staff, expand the system to include more types of exams and diseases, and explore new artificial intelligence techniques for future improvements. Future researchers should continue exploring and evaluating advanced artificial intelligence and machine learning techniques to further enhance predictive health systems.
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    “Implementación de un sistema predictivo de enfermedades mediante el análisis exámenes médicos con la utilización de inteligencia artificial para el Laboratorio Salud Familiar del cantón La Maná”
    (Ecuador : La Maná Universidad Técnica de Cotopaxi (UTC), 2024-08) Osorio Muñoz, Letty Fernanda; Suntasig Jaen, Micaela Nicole; Cajas, Jaime Mesías
    The general objective of this research was to develop a disease-predictive system through the analysis of medical exams using artificial intelligence to improve diagnostic accuracy and efficiency in medical care at the “Salud Familiar” Laboratory in La Maná. The specific objectives included reviewing bibliographic sources to establish a solid theoretical foundation, designing the system using the agile SCRUM methodology and advanced technological tools such as Python, Django, SQLite3, and Scikit-learn, and conducting extensive testing to evaluate the system's accuracy, efficiency, and reliability. The research conclusions were favorable. The bibliographic review provided a robust theoretical foundation, which was fundamental for the successful implementation of the system. The system design was carried out in a structured and efficient manner, ensuring flexibility and adaptability for future improvements. Extensive testing confirmed that the system is highly accurate, efficient, and reliable, significantly enhancing the laboratory's ability to offer early and precise diagnoses. The system's impacts are positive. Technically, it represents a significant advancement by integrating cutting-edge technologies into clinical data management. Socially, it improves the quality of medical care, directly benefiting the community of La Maná. Environmentally, it optimizes resource use and reduces medical waste and paper consumption. Economically, it reduces costs associated with unnecessary tests and incorrect treatments, benefiting both the laboratory and patients. It is recommended to continuously maintain and update the system, train the laboratory staff, expand the system to include more types of exams and diseases, and explore new artificial intelligence techniques for future improvements. Future researchers should continue exploring and evaluating advanced artificial intelligence and machine learning techniques to further enhance predictive health systems.