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metadata
license: mit
datasets:
  - prabinpanta0/genki_hospital
pipeline_tag: text-classification
tags:
  - patient-readmission
  - predictive-model
  - healthcare
  - sklearn
  - keras
  - LightGBM
language:
  - en
model_type: classification
task: patient-readmission-prediction
app_mode: standalone
frameworks:
  - scikit-learn
  - tensorflow
metrics:
  - accuracy
  - precision
  - recall
  - f1
  - roc_auc

Patient Readmission Prediction

Tranning

Github: prabinpanta0/Patient-Readmission-Prediction

Dataset

{
  "model_id": "prabinpanta0/Patient-Readmission-Prediction",
  "model_type": "sequence-classification",
  "library": {
    "random_forest": "scikit-learn",
    "logistic_regression": "scikit-learn",
    "k_nearest": "scikit-learn",
    "svc": "scikit-learn",
    "naive_bayes": "scikit-learn",
    "neural_network": "keras",
    "cross_validation_random_forest": "scikit-learn",
    "cross_validation_logistic_regression": "scikit-learn",
    "cross_validation_lightgbm": "LightGBM"
  },
  "model_architectures": {
    "random_forest": "RandomForestClassifier",
    "logistic_regression": "LogisticRegression",
    "k_nearest": "KNeighborsClassifier",
    "svc": "SVC",
    "naive_bayes": "MultinomialNB",
    "neural_network": "NeuralNetwork",
    "cross_validation_random_forest": "RandomForestClassifier",
    "cross_validation_logistic_regression": "LogisticRegression",
    "cross_validation_lightgbm": "LGBMClassifier"
  },
  "model_paths": {
    "random_forest": "model_RandomForestClassifier.pkl",
    "logistic_regression": "model_Logistic_Regression.pkl",
    "k_nearest": "model_K_nearest.pkl",
    "svc": "model_svc.pkl",
    "naive_bayes": "model_naive_bayes.pkl",
    "neural_network": "neural_network.keras",
    "cross_validation_random_forest": "model_rf.pkl",
    "cross_validation_logistic_regression": "model_lr.pkl",
    "cross_validation_lightgbm": "model_lgbm.pkl"
  },
  "model_classes": {
    "random_forest": "RandomForestClassifier",
    "logistic_regression": "LogisticRegression",
    "k_nearest": "KNeighborsClassifier",
    "svc": "SVC",
    "naive_bayes": "MultinomialNB",
    "neural_network": "NeuralNetwork",
    "cross_validation_random_forest": "RandomForestClassifier",
    "cross_validation_logistic_regression": "LogisticRegression"
  },
  "model_configs": {
    "random_forest": {
      "n_estimators": 100,
      "max_depth": 5
    },
    "logistic_regression": {
      "C": 1,
      "max_iter": 1000
    },
    "k_nearest": {
      "n_neighbors": 5
    },
    "svc": {
      "C": 1,
      "kernel": "linear"
    },
    "naive_bayes": {
      "alpha": 1
    },
    "neural_network": {
      "input_dim": 10,
      "output_dim": 1,
      "hidden_dim": 10
    },
    "cross_validation_random_forest": {
      "n_estimators": 100,
      "max_depth": 5
    },
    "cross_validation_logistic_regression": {
      "C": 1,
      "max_iter": 1000
    },
    "cross_validation_lightgbm": {
      "random_state": 42
    }
  }
}

metrics

Model Accuracy Precision Recall AUC-ROC
Random Forest 0.86544 0.8734358240972471 0.8337883959044369 0.8635809449401703
Logistic Regression 0.74736 0.7493540051679587 0.6928327645051194 0.7441573461079813
K-Nearest Neighbors 0.84112 0.8543724844493231 0.7969283276450512 0.838524404786381
Support Vector Classifier 0.84256 0.8492462311557789 0.8075085324232082 0.8405012541634113
Naive Bayes 0.74176 0.7692307692307693 0.6416382252559727 0.7358793535918418
Neural Network 0.87664 0.889009009009009 0.8419795221843004 0.8746042189234755
Random Forest (Cross-Validation) 0.86544 0.8734358240972471 0.8337883959044369 0.8635809449401703
Logistic Regression (Cross-Validation) 0.74736 0.7493540051679587 0.6928327645051194 0.7441573461079813
LightGBM (Cross-Validation) 0.8728 0.8773418168964299 0.847098976109215 0.8712904519100293
Random Forest Logistic Regression K-Nearest Neighbors Support Vector Classifier Naive Bayes Neural Network Random Forest (Cross-Validation) Logistic Regression (Cross-Validation) LightGBM (Cross-Validation)
1.0 0.7453866666666666 0.8901866666666667 0.8530133333333333 0.7455466666666667 0.88288 1.0 0.7453866666666666 0.9045866666666667
1.0 0.7449201741654572 0.9005328596802842 0.8556024378809189 0.7743332882090158 0.8964114832535885 1.0 0.7449201741654572 0.910874897792314
1.0 0.6979827742520399 0.8618540344514959 0.8272892112420671 0.6482320942883046 0.849274705349048 1.0 0.6979827742520399 0.8837262012692656
1.0 0.7427552396345833 0.8886139001594574 0.8515853672571407 0.7401446588709305 0.8810145438895148 1.0 0.7427552396345833 0.9034286859660855