{"id":1485,"date":"2025-02-24T08:29:41","date_gmt":"2025-02-24T08:29:41","guid":{"rendered":"https:\/\/mariogr.com\/?p=1485"},"modified":"2026-11-03T10:29:17","modified_gmt":"2026-11-03T10:29:17","slug":"introduccion-al-machine-learning","status":"publish","type":"portfolio","link":"https:\/\/mariogr.com\/index.php\/portfolio-archive\/introduccion-al-machine-learning\/","title":{"rendered":"Introducci\u00f3n al Machine Learning"},"content":{"rendered":"\t\t
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Machine Learning y L\u00f3gica Difusa en la Medicina y la Seguridad Alimentaria<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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La inteligencia artificial ha revolucionado numerosos campos, y la medicina y la seguridad alimentaria no son la excepci\u00f3n. En este proyecto, combin\u00e9 t\u00e9cnicas avanzadas de Machine Learning (ML)<\/strong> y l\u00f3gica difusa<\/strong> para abordar dos problemas clave junto a un compa\u00f1ero:<\/p>\n

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  1. El diagn\u00f3stico autom\u00e1tico de enfermedades dermatol\u00f3gicas<\/strong> a partir de s\u00edntomas cl\u00ednicos.<\/li>\n
  2. La identificaci\u00f3n de hongos venenosos y comestibles<\/strong> usando modelos de aprendizaje autom\u00e1tico.<\/li>\n<\/ol>\n

    Ambos casos presentan desaf\u00edos \u00fanicos que fueron abordados con diversas metodolog\u00edas, desde modelos tradicionales de clasificaci\u00f3n hasta redes neuronales profundas con Keras<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Diagn\u00f3stico de Enfermedades Dermatol\u00f3gicas con Machine Learning<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Preprocesamiento y An\u00e1lisis de Datos<\/strong><\/h3>

    Para este estudio, utilic\u00e9 un conjunto de datos cl\u00ednicos de dermatolog\u00eda, proces\u00e1ndolo para eliminar valores nulos y normalizar variables. A trav\u00e9s de t\u00e9cnicas de exploraci\u00f3n de datos (EDA)<\/strong> como boxplots, violinplots y matrices de correlaci\u00f3n<\/strong>, pude visualizar patrones relevantes en distintas enfermedades cut\u00e1neas.<\/p>

    Uno de los hallazgos clave fue la alta correlaci\u00f3n entre ciertas caracter\u00edsticas, como el enrojecimiento y la descamaci\u00f3n, lo que permiti\u00f3 afinar los modelos de clasificaci\u00f3n.<\/p>

    Comparaci\u00f3n de Modelos de Machine Learning<\/strong><\/h3>

    Prob\u00e9 distintos modelos supervisados para clasificar las enfermedades dermatol\u00f3gicas en seis categor\u00edas:<\/p>

    • Random Forest<\/strong> (precisi\u00f3n del 98%)<\/li>
    • Gradient Boosting<\/strong> (precisi\u00f3n del 96%)<\/li>
    • Support Vector Machine (SVM)<\/strong> (precisi\u00f3n del 70%)<\/li>
    • \u00c1rboles de Decisi\u00f3n<\/strong> (precisi\u00f3n del 93%)<\/li><\/ul>

      El modelo Random Forest<\/strong> fue el m\u00e1s efectivo y se mejor\u00f3 a\u00fan m\u00e1s con Bagging<\/strong>, una t\u00e9cnica que reduce la varianza en la clasificaci\u00f3n. Tambi\u00e9n utilic\u00e9 matrices de confusi\u00f3n<\/strong> para analizar los errores y detectar clases dif\u00edciles de predecir.<\/p>

      Red Neuronal con Keras<\/strong><\/h3>

      A continuaci\u00f3n, implement\u00e9 una red neuronal con Keras<\/strong>, optimizando su arquitectura para clasificaci\u00f3n multiclase. Tras probar distintas configuraciones de neuronas y capas ocultas, determin\u00e9 que los mejores resultados se obten\u00edan con arquitecturas de (30,20) y (40,30) neuronas<\/strong>, alcanzando m\u00e9tricas superiores al 97% en precisi\u00f3n, recall y F1-score<\/strong>.<\/p>

      Si bien la red neuronal tuvo un rendimiento similar a Random Forest<\/strong>, su principal ventaja radica en la capacidad de generalizaci\u00f3n y detecci\u00f3n de patrones m\u00e1s complejos.<\/p>

      L\u00f3gica Difusa para Diagn\u00f3stico M\u00e9dico<\/strong><\/h3>

      Adem\u00e1s del enfoque basado en ML, desarroll\u00e9 un sistema de l\u00f3gica difusa<\/strong> que permite diagnosticar enfermedades a partir de s\u00edntomas introducidos por el usuario. Este m\u00e9todo es ideal en situaciones donde los s\u00edntomas no son categ\u00f3ricos, sino que var\u00edan en intensidad.<\/p>

      Por ejemplo, en lugar de decir que un paciente tiene o no fiebre<\/strong>, se pueden establecer rangos como leve, moderada o alta<\/strong>, permitiendo un diagn\u00f3stico m\u00e1s flexible.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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      Identificaci\u00f3n de Hongos Comestibles y Venenosos con Machine Learning<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      Importancia del Problema<\/strong><\/h3>

      La identificaci\u00f3n err\u00f3nea de hongos silvestres puede ser letal. Existen especies visualmente similares entre s\u00ed, pero con efectos dr\u00e1sticamente opuestos: algunas son nutritivas y seguras, mientras que otras contienen toxinas mortales.<\/p>

      El uso de Machine Learning<\/strong> permite desarrollar un sistema autom\u00e1tico que analiza caracter\u00edsticas como color, forma, textura y h\u00e1bitat para determinar si un hongo es seguro o peligroso<\/strong>.<\/p>

      Modelo de Clasificaci\u00f3n para Hongos<\/strong><\/h3>

      Para este an\u00e1lisis, utilic\u00e9 un dataset con caracter\u00edsticas extra\u00eddas de distintos hongos. Se probaron m\u00faltiples modelos, incluyendo:<\/p>

      • Random Forest<\/strong> \u2192 Precisi\u00f3n del 99.2%<\/strong><\/li>
      • SVM<\/strong> \u2192 Precisi\u00f3n del 95.7%<\/strong><\/li>
      • Red Neuronal con Keras<\/strong> \u2192 Precisi\u00f3n del 98.5%<\/strong><\/li><\/ul>

        Los modelos basados en \u00e1rboles de decisi\u00f3n como Random Forest<\/strong> fueron los m\u00e1s efectivos, ya que pueden manejar de manera eficiente atributos categ\u00f3ricos como el color de las esporas<\/strong> o la presencia de volva en la base del tallo<\/strong>.<\/p>

        Problema y Riesgos<\/strong><\/h3>

        Los hongos venenosos pueden causar intoxicaciones graves si se confunden con especies comestibles. Para abordar esto, entren\u00e9 modelos de Machine Learning capaces de identificar hongos seguros y peligrosos<\/strong> a partir de caracter\u00edsticas como:<\/p>

        • Color de esporas<\/strong><\/li>
        • Forma y textura del sombrero<\/strong><\/li>
        • Presencia de volva o anillo en el tallo<\/strong><\/li><\/ul>

          Entrenamiento y Evaluaci\u00f3n de Modelos<\/strong><\/h3>

          Los modelos se entrenaron con un conjunto de datos de hongos clasificados y se validaron con una partici\u00f3n de prueba del 20% de los datos<\/strong> para medir su efectividad.<\/p>
          Modelo<\/th>Precisi\u00f3n<\/th>Recall<\/th>F1-score<\/th><\/tr>
          Random Forest<\/td>99.2%<\/td>99.0%<\/td>99.1%<\/td><\/tr>
          Support Vector Machine<\/td>95.7%<\/td>94.8%<\/td>95.1%<\/td><\/tr>
          Red Neuronal (Keras)<\/td>98.5%<\/td>98.3%<\/td>98.4%<\/td><\/tr><\/tbody><\/table>

          Al igual que en el caso de dermatolog\u00eda, Random Forest<\/strong> ofreci\u00f3 los mejores resultados, clasificando correctamente la mayor\u00eda de los hongos con un 99.2% de precisi\u00f3n<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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