المؤلفون: Ortiz-Letechipia, Jacqueline S., Galvan-Tejada, Carlos E., Galván-Tejada, Jorge I., Soto-Murillo, Manuel A., Acosta-Cruz, Erika, Gamboa-Rosales, Hamurabi, Celaya Padilla, José María, Luna-García, Huizilopoztli

المصدر: PeerJ ; volume 12, page e16501 ; ISSN 2167-8359

الوصف: The occurrence of fungi is cosmopolitan, and while some mushroom species are beneficial to human health, others can be toxic and cause illness problems. This study aimed to analyze the organoleptic, ecological, and morphological characteristics of a group of fungal specimens and identify the most significant features to develop models for fungal toxicity classification using genetic algorithms and LASSO regression. The results of the study indicated that odor, spore print color, and habitat were the most significant characteristics identified by the genetic algorithm GALGO. Meanwhile, odor, gill size, stalk shape, and twelve other features were the relevant characteristics identified by LASSO regression. The importance score of the odor variable was 99.99%, gill size obtained 73.7%, stalk shape scored 39.9%, and the remaining variables did not score higher than 18%. Logistic regression, k-nearest neighbor (KNN), and XG-Boost classification algorithms were used to develop models using the features selected by both GALGO and LASSO. The models were evaluated using sensitivity, specificity, and accuracy metrics. The models with the highest AUC values were XGBoost, with a maximum value of 0.99 using the features selected by LASSO, followed by KNN with a maximum value of 0.99. The GALGO selection resulted in a maximum AUC of 0.98 in KNN and XGBoost. The models developed in this study have the potential to aid in the accurate identification of toxic fungi, which can prevent health problems caused by their consumption.

الإتاحة: https://doi.org/10.7717/peerj.16501Test
https://peerj.com/articles/16501.pdfTest
https://peerj.com/articles/16501.xmlTest
https://peerj.com/articles/16501.htmlTest

المؤلفون: de Avila-Armenta, Eduardo, Celaya-Padilla, José M., Galván-Tejada, Jorge I., Soto-Murillo, Manuel A., Hernandez-Guitierrez, Andrés, Alvarado-Padilla, José J., Ríos-Ríos, Jose I., Martinez-Torteya, Antonio

المصدر: Procedia Computer Science ; volume 224, page 106-113 ; ISSN 1877-0509

مصطلحات موضوعية: General Earth and Planetary Sciences, General Environmental Science

الإتاحة: https://doi.org/10.1016/j.procs.2023.09.017Test
https://api.elsevier.com/content/article/PII:S1877050923010645?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1877050923010645?httpAccept=text/plainTest

المؤلفون: Villagrana-Bañuelos, Karen E., Galván-Tejada, Carlos E., Galván-Tejada, Jorge I., Gamboa-Rosales, Hamurabi, Celaya-Padilla, José M., Soto-Murillo, Manuel A., Solís-Robles, Roberto

المصدر: Healthcare (2227-9032); Jul2022, Vol. 10 Issue 7, p1303-1303, 13p

مصطلحات موضوعية: LIPID metabolism, SUPPORT vector machines, BIOMARKERS, DECISION trees, METABOLOMICS, MACHINE learning, RANDOM forest algorithms, SUDDEN infant death syndrome, LOGISTIC regression analysis, SENSITIVITY & specificity (Statistics), PREDICTION models, ALGORITHMS

مستخلص: Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology. [ABSTRACT FROM AUTHOR]

: Copyright of Healthcare (2227-9032) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Soto-Murillo, Manuel A., Villagrana Bañuelos, Karen E., Rodriguez-Ruiz, Julieta G., Salinas-González, Jared D., Galván-Tejada, Carlos E., Gamboa-Rosales, Hamurabi, Galván-Tejada, Jorge I.

المصدر: IFMBE Proceedings ; VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering ; page 104-112 ; ISSN 1680-0737 1433-9277 ; ISBN 9783030306472 9783030306489

الإتاحة: https://doi.org/10.1007/978-3-030-30648-9_15Test

المؤلفون: Soto Murillo, Manuel Alejandro, de la Rosa Vargas, José Ismael, Moreno Báez, Arturo

المصدر: Congreso Internacional de Investigacion Academia Journals; 2018, Vol. 10 Issue 3, p3369-3374, 5p

Abstract (Spanish): En este artículo, se presenta una comparación de las técnicas clásicas de parametrización; Codificación Predictiva Lineal (LPC) y Coeficientes Cepstrales de Frecuencias-Mel (MFCC), implementadas en la etapa de extracción de características en los Sistemas de Reconocimiento Automático de Voz (SRAV) para obtener los coeficientes que mejor caractericen la señal de voz. Las señales de voz se muestrearon a 8 y 16kHz y se varió el número de coeficientes característicos (8-12 para 8kHz y 16-24 para 16kHz) para encontrar la configuración que brinde la mayor tasa de reconocimiento y el menor consumo de recursos (tiempo y cálculo). En la etapa de modelado se usó la técnica Modelos Ocultos de Markov (HMM). La técnica de parametrización MFCC presentó una tasa de reconocimiento superior que la técnica LPC bajo las mismas condiciones, obteniendo tasas de reconocimiento de hasta 99.66%. [ABSTRACT FROM AUTHOR]

: Copyright of Congreso Internacional de Investigacion Academia Journals is the property of PDHTech, LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Soto-Murillo, Manuel A., Galván-Tejada, Jorge I., Galván-Tejada, Carlos E., Celaya-Padilla, Jose M., Luna-García, Huizilopoztli, Magallanes-Quintanar, Rafael, Gutiérrez-García, Tania A., Gamboa-Rosales, Hamurabi, Chen, Tin-Chih Toly

المصدر: Healthcare (2227-9032); Mar2021, Vol. 9 Issue 3, p317, 1p

مصطلحات موضوعية: LINEAR predictive coding, HEART sounds, ARTIFICIAL neural networks, HEART murmurs, SUPPORT vector machines

مصطلحات جغرافية: MEXICO

الشركة/الكيان: WORLD Health Organization

مستخلص: The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems. [ABSTRACT FROM AUTHOR]

: Copyright of Healthcare (2227-9032) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)