ARTIFICIAL INTELLIGENCE’S SIX MODELS CROSS-VALIDATION ON ALZHEIMER PATTERNS RECOGNITION USING ARTIFICIAL INTELLIGENCE

Miguel Patiã‘o Ortiz, Ricardo Carreã‘o Aguilera, Daniel Pacheco Bautista and Julian Patiã‘o Ortiz
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Miguel Patiã‘o Ortiz: Instituto Politécnico Nacional — SEPI ESIME Zacatenco, Unidad Profesional Adolfo López Mateos, Zacatenco. Alcaldía Gustavo A. Madero, C. P. 07738, Ciudad de México, México
Ricardo Carreã‘o Aguilera: ��Universidad del Istmo — UNISTMO, Ciudad Universitaria S/N, Barrio Santa Cruz 4a, Sección Sto. Domingo Tehuantepec, C. P. 70760, Oaxaca, México
Daniel Pacheco Bautista: ��Universidad del Istmo — UNISTMO, Ciudad Universitaria S/N, Barrio Santa Cruz 4a, Sección Sto. Domingo Tehuantepec, C. P. 70760, Oaxaca, México
Julian Patiã‘o Ortiz: Instituto Politécnico Nacional — SEPI ESIME Zacatenco, Unidad Profesional Adolfo López Mateos, Zacatenco. Alcaldía Gustavo A. Madero, C. P. 07738, Ciudad de México, México

FRACTALS (fractals), 2025, vol. 33, issue 07, 1-8

Abstract: The early detection of Alzheimer’s disease remains a critical challenge in medical research due to the complexity of its symptoms and the late-stage diagnosis commonly observed in clinical settings. This paper explores artificial intelligence models cross-validation, specifically a deep learning model based on the Faster-RCNN-ResNet-Coco 101 architecture versus others not using deep learning: decision Tree, random forest, support vector machine, XGBoost, and voting classifier to facilitate early diagnosis. By leveraging data from the ADNI open database, our study implements a comprehensive training process within a Python Conda environment, demonstrating promising classification accuracy and validation results. While various recognition systems exist, our approach provides an expert, ad hoc solution tailored to the early identification of Alzheimer’s patterns through tomography analysis based on Faster-RCNN-ResNet-Coco 101 architecture as the best choice in the tested models.

Keywords: Artificial Intelligence; ADNI Database; Faster-RCNN-ResNet-Coco 101; Deep Learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25500616

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