Abstract
The prevalence and impact of diabetes in Mexico are thoroughly examined in this study. Over the past four decades, diabetes has emerged as the predominant health concern in the country, ranking as the leading cause of death in women and the second in men since 2000. It has also been identified as the primary culprit behind premature retirement, blindness, and kidney failure. Projections indicate that by 2025, nearly 11.7 million Mexicans could be diagnosed with diabetes, underscoring the urgency of understanding and addressing this escalating health crisis [1]. Previous research on diabetes characteristics and consequences among individuals aged 20 to 40 has primarily relied on hospital-based samples, potentially skewing results toward severe cases or specific ethnic groups. A critical gap exists in nationwide, population-based studies that can provide a more comprehensive understanding of the prevalence and characteristics of early-onset type 2 diabetes.
Given that 79% of Mexico’s population is under 40 years old [2], there is an imperative need for such studies to inform targeted preventive measures. This study aims to fill this gap by predicting the risk index for the general population based on diabetes incidence data collected by a delegation in Mexico through public health institutions. The prediction will leverage a multilayer perceptron neural network to enhance the accuracy and applicability of the findings.
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Acknowledgment
We sincerely thank the Instituto Tecnológico de Tijuana for their invaluable support and essential contribution to our research as students of the Master’s program in Information Technologies. The institution has provided a conducive environment for developing our projects, offering resources and guidance crucial for our academic and professional growth. Additionally, we extend our recognition and thanks to the Instituto Mexicano del Seguro Social (IMSS) for their generous collaboration in providing the necessary information for our research. The availability of accurate and relevant data from IMSS has been instrumental in the success of our work, allowing us to address the challenges posed by our research comprehensively.
We are deeply grateful for the support from both institutions, whose contributions have been pivotal in our academic journey and in achieving our research objectives. This invaluable support has strengthened our foundation as professionals in information technologies, and we are committed to using the knowledge gained to contribute to advancing science and technology. Our gratitude also extends to all those who have participated in this process and made this enriching experience possible.
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Cárdenas-Isla, H., Reyes-Osorio, R.L., Jacobo-Rojas, A., Robles-Gallegos, A., Márquez, B.Y. (2024). Incidence Assessment of Diabetes by Delegation in the United Mexican States Applying the Multilayer Perceptron Neural Network. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-031-60215-3_6
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