Merge branch 'develop' into Recursive-feature-elimination

README.md

@@ -38,7 +38,7 @@ However, if the thyroid gland is underactive (hypothyroidism) or overactive (hyp
 
 We used the above attributes to build a model that can classify patients with ESS with high accuracy. These are chosen because they are the most important attributes in the dataset. Moreover, theses attributes can be measured in a blood test. 
 
-## Part 1: Results 
+## Part 1: Results : IVCobiCET 
 
 We used 4 different machine learning approaches to build a model that can classify patients with ESS with high accuracy. The approaches are:
 
@@ -58,6 +58,30 @@ The results are shown in the table below:
 
 [Access the detailed results](https://github.com/cilab-ufersa/euthyroid_sick_syndrome/blob/develop/euthyroid_sick_syndrome/notebooks/IVCoBiCET_results.ipynb)
 
+## Part 2: Results : ICISNA 2023
+
+We used 4 different machine learning approaches to build a model that can classify patients with ESS with high accuracy. The approaches are:
+
+  * Logistic Regression
+  * Random Forest
+  * LightGBM
+  * XGBoost
+  * Stack Ensemble based on Random Forest and XGBoost
+
+The results are shown in the table below:
+
+| Approach | Accuracy | Recall |  Precision | F1-Score |
+| ------ | ------ | ------ | ------ | ------ |
+| Logistic Regression | 91.98%     |   93.21%  | 90.62%  |   91.90%     
+| Random Forest |  98.34%    | 98.21%  | 98.38%     | 98.30%
+| LightGBM | 97.64%      |   97.32%     |      97.64%    |     97.58%  
+| XGBoost | 98.60%      | 98.77%      |      98.57%     |    98.57%
+| Stack Ensemble | 98.78%       | 98.75%        |   98.75%          |  98.75%      
+
+* [Access the detailed results](https://github.com/cilab-ufersa/euthyroid_sick_syndrome/blob/develop/euthyroid_sick_syndrome/notebooks/ICISNA23.ipynb)
+
+* [Web system](https://github.com/cilab-ufersa/euthyroid_diagnostic_support_app)
+
 ## Scientific Developers
 👤 [Vinicius Almeida](https://github.com/vinicius-a-almeida): 
   _vinicius45anacleto@gmail.com_

euthyroid_sick_syndrome/models/neural.py

@@ -18,19 +18,24 @@
 
 dataset_res, ouput_label = balance_dataset_smote(dataset, output_label_dataset, random_state=42, k_neighbors=5)
 
+
+
+print(dataset_res.shape)
+
+
 # Dividindo os dados em treino e teste em 80% e 20% respectivamente
 # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=3, stratify=y)
 input_train, input_test, output_train, output_test = slipt_and_standardize_dataset(dataset=dataset_res, output_label=ouput_label)
 
 # Modelo de rede perceptron multicamadas com uma camada oculta de 64 neurônios
-# e camada de saída com 3 neurônios (3 classes)
+# e camada de saída com 1 neurônios (1 classes)
+
 model = tf.keras.models.Sequential()
-model.add(tf.keras.Input(shape=(input_train.shape[1],)))
-model.add(tf.keras.layers.Dense(128, activation='sigmoid'))
-model.add(tf.keras.layers.Dense(256, activation='sigmoid'))
-model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
+model.add(tf.keras.layers.Dense(64, input_dim=8, activation='sigmoid'))
+model.add(tf.keras.layers.Dense(1, activation='softmax'))
 model.summary() #visualizando o modelo
 
+
 # Compile model
 # Otimizador Adam com taxa de aprendizado de 0.01
 opt = tf.keras.optimizers.Adam(learning_rate=0.01)

euthyroid_sick_syndrome/neural_network/history.csv

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