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Holguin-Garcia SA, Guevara-Navarro E, Daza-Chica AE, Patiño-Claro MA, Arteaga-Arteaga HB, Ruz GA, Tabares-Soto R, Bravo-Ortiz MA. A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure. BMC Med Inform Decis Mak 2024; 24:60. [PMID: 38429718 PMCID: PMC10908140 DOI: 10.1186/s12911-024-02460-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/13/2024] [Indexed: 03/03/2024] Open
Abstract
INTRODUCTION Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy. METHOD To optimize these processes and make them more efficient, we have resorted to innovative artificial intelligence methods essential in classifying EEG signals. For this, comparing traditional models, such as machine learning or deep learning, with cutting-edge models, in this case, using Capsule-Net architectures and Transformer Encoder, has a crucial role in finding the most accurate model and helping the doctor to have a faster diagnosis. RESULT In this paper, a comparison was made between different models for binary and multiclass classification of the epileptic seizure detection database, achieving a binary accuracy of 99.92% with the Capsule-Net model and a multiclass accuracy with the Transformer Encoder model of 87.30%. CONCLUSION Artificial intelligence is essential in diagnosing pathology. The comparison between models is helpful as it helps to discard those that are not efficient. State-of-the-art models overshadow conventional models, but data processing also plays an essential role in evaluating the higher accuracy of the models.
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Affiliation(s)
| | - Ernesto Guevara-Navarro
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
| | - Alvaro Eduardo Daza-Chica
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
| | - Maria Alejandra Patiño-Claro
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
| | - Harold Brayan Arteaga-Arteaga
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
| | - Gonzalo A Ruz
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, 7941169, Chile
- Center of Applied Ecology and Sustainability (CAPES), Santiago, 8331150, Chile
- Data Observatory Foundation, Santiago, 7510277, Chile
| | - Reinel Tabares-Soto
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
- Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, 170004, Caldas, Colombia
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, 7941169, Chile
| | - Mario Alejandro Bravo-Ortiz
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia.
- Centro de Bioinformática y Biología Computacional (BIOS), Manizales, 170001, Colombia.
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Arteaga-Arteaga HB, Candamil-Cortés MS, Breaux B, Guillen-Rondon P, Orozco-Arias S, Tabares-Soto R. Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data. Brief Funct Genomics 2023; 22:428-441. [PMID: 37119295 DOI: 10.1093/bfgp/elad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/17/2023] [Indexed: 05/01/2023] Open
Abstract
Artificial intelligence is revolutionizing all fields that affect people's lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to develop effective therapies. Due to advances in single-cell RNA sequencing (scRNA-seq), it is possible to understand the cellular and molecular heterogeneity in the GBM. Given that there are different cell groups in these tumors, there is a need to apply Machine Learning (ML) algorithms. It will allow extracting information to understand how cancer changes and broaden the search for effective treatments. We proposed multiple comparisons of ML algorithms to classify cell groups based on the GBM scRNA-seq data. This broad comparison spectrum can show the scientific-medical community which models can achieve the best performance in this task. In this work are classified the following cell groups: Tumor Core (TC), Tumor Periphery (TP) and Normal Periphery (NP), in binary and multi-class scenarios. This work presents the biomarker candidates found for the models with the best results. The analyses presented here allow us to verify the biomarker candidates to understand the genetic characteristics of GBM, which may be affected by a suitable identification of GBM heterogeneity. This work obtained for the four scenarios covered cross-validation results of $93.03\% \pm 5.37\%$, $97.42\% \pm 3.94\%$, $98.27\% \pm 1.81\%$ and $93.04\% \pm 6.88\%$ for the classification of TP versus TC, TP versus NP, NP versus TP and TC (TPC) and NP versus TP versus TC, respectively.
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Affiliation(s)
| | - Mariana S Candamil-Cortés
- Departamento de Ciencias Computacionales, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Centro de Investigaciones en Medio Ambiente y Desarrollo - CIMAD, Universidad de Manizales, Manizales, Caldas, Colombia
| | - Brian Breaux
- Department of Computer Science, University of Houston Downtown, Houston, Texas, United States of America
| | - Pablo Guillen-Rondon
- Department of Computer Science, University of Houston Downtown, Houston, Texas, United States of America
- Biomedical and Energy Solutions LLC, Houston, Texas, United States of America
| | - Simon Orozco-Arias
- Departamento de Ciencias Computacionales, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, Caldas, Colombia
| | - Reinel Tabares-Soto
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, Caldas, Colombia
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Arteaga-Arteaga HB, Mora-Rubio A, Florez F, Murcia-Orjuela N, Diaz-Ortega CE, Orozco-Arias S, delaPava M, Bravo-Ortíz MA, Robinson M, Guillen-Rondon P, Tabares-Soto R. Machine learning applications to predict two-phase flow patterns. PeerJ Comput Sci 2021; 7:e798. [PMID: 34909465 PMCID: PMC8641572 DOI: 10.7717/peerj-cs.798] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 11/07/2021] [Indexed: 05/15/2023]
Abstract
Recent advances in artificial intelligence with traditional machine learning algorithms and deep learning architectures solve complex classification problems. This work presents the performance of different artificial intelligence models to classify two-phase flow patterns, showing the best alternatives for this specific classification problem using two-phase flow regimes (liquid and gas) in pipes. Flow patterns are affected by physical variables such as superficial velocity, viscosity, density, and superficial tension. They also depend on the construction characteristics of the pipe, such as the angle of inclination and the diameter. We selected 12 databases (9,029 samples) to train and test machine learning models, considering these variables that influence the flow patterns. The primary dataset is Shoham (1982), containing 5,675 samples with six different flow patterns. An extensive set of metrics validated the results obtained. The most relevant characteristics for training the models using Shoham (1982) dataset are gas and liquid superficial velocities, angle of inclination, and diameter. Regarding the algorithms, the Extra Trees model classifies the flow patterns with the highest degree of fidelity, achieving an accuracy of 98.8%.
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Affiliation(s)
| | - Alejandro Mora-Rubio
- Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
| | - Frank Florez
- Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
| | - Nicolas Murcia-Orjuela
- Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
| | | | - Simon Orozco-Arias
- Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Department of Systems and Informatics, Universidad de Caldas, Manizales, Caldas, Colombia
| | - Melissa delaPava
- Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
| | - Mario Alejandro Bravo-Ortíz
- Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
| | - Melvin Robinson
- College of Science and Engineering, Houston Baptist University, Houston, Texas, United States of America
| | - Pablo Guillen-Rondon
- Department of Computer Science, University of Houston Downtown, Houston, Texas, United States of America
- Biomedical and Energy Solutions LLC, Houston, Texas, United States of America
| | - Reinel Tabares-Soto
- Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
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