Predicción del desenlace terapéutico de la leishmaniasis utilizando aprendizaje automático sobre SNPs
Machine learning has enabled significant advancements in the field of medicine, particularly in predicting the outcome of disease treatment. This case study explores machine learning models and techniques to predict the success or failure of leishmaniasis treatment based on single nucleotide Polymor...
- Autores:
-
Bertín Sánchez, Alvaro José
Caicedo Rojas, Santiago
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Pontificia Universidad Javeriana Cali
- Repositorio:
- Vitela
- Idioma:
- spa
- OAI Identifier:
- oai:vitela.javerianacali.edu.co:11522/2549
- Acceso en línea:
- https://vitela.javerianacali.edu.co/handle/11522/2549
- Palabra clave:
- Rights
- License
- https://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | Machine learning has enabled significant advancements in the field of medicine, particularly in predicting the outcome of disease treatment. This case study explores machine learning models and techniques to predict the success or failure of leishmaniasis treatment based on single nucleotide Polymorphisms (snps) in dna sequences. This is crucial because leishmaniasis treatment can have adverse effects for the human body, making it essential to predict whether individuals with leishmaniasis should undergo treatment. Unsupervised machine learning techniques were employed for the selection of the most significant snps. Subsequently, supervised learning techniques were utilized for prediction, and the performance of the model was assessed. This comprehensive approach aims to determine the efficacy of leishmaniasis treatment and whether individuals should or should not undergo the prescribed regimen. |
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