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Merge pull request #712 from adi271001/sepsis-survival-prediction
Sepsis survival prediction
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Sepsis Survival Prediction/Dataset/s41598-020-73558-3_sepsis_survival_primary_cohort.csv
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Sepsis Survival Prediction/Dataset/s41598-020-73558-3_sepsis_survival_study_cohort.csv
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Sepsis Survival Prediction/Dataset/s41598-020-73558-3_sepsis_survival_validation_cohort.csv
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# Sepsis Survival Prediction - Models | ||
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## Models Implemented | ||
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### 1. Random Forest | ||
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**Description**: Random Forest is an ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes as the prediction. | ||
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### 2. XGBoost | ||
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**Description**: XGBoost is an optimized gradient boosting framework that is highly efficient and scalable, often used for structured data. | ||
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### 3. Logistic Regression | ||
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**Description**: Logistic Regression is a linear model that uses the logistic function to model the probability of a binary class. | ||
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### 4. Gradient Boosting | ||
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**Description**: Gradient Boosting builds models sequentially, with each new model correcting errors made by the previous ones. | ||
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### 5. AdaBoost | ||
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**Description**: AdaBoost is an ensemble learning method that combines multiple weak classifiers to create a strong classifier. | ||
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### 6. CatBoost | ||
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**Description**: CatBoost is a gradient boosting algorithm that handles categorical features automatically and efficiently. | ||
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### 7. LightGBM | ||
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**Description**: LightGBM is a gradient boosting framework designed for efficiency with large datasets and low memory usage. | ||
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### 8. K-Nearest Neighbors (KNN) | ||
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**Description**: K-Nearest Neighbors is a simple, instance-based learning algorithm where classification is based on the majority vote of the nearest neighbors. | ||
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### 9. Support Vector Machine (SVM) | ||
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**Description**: SVM is a supervised learning model that finds the hyperplane which best divides a dataset into classes. | ||
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### 10. Decision Tree | ||
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**Description**: Decision Trees are non-parametric supervised learning methods used for classification based on simple decision rules inferred from the data features. | ||
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## Performance of the Models based on the Accuracy Scores | ||
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- **Random Forest**: 90.92% | ||
- **XGBoost**: 90.92% | ||
- **Logistic Regression**: 90.92% | ||
- **Gradient Boosting**: 90.92% | ||
- **AdaBoost**: 90.92% | ||
- **CatBoost**: 90.93% | ||
- **LightGBM**: 90.92% | ||
- **K-Nearest Neighbors (KNN)**: 90.46% | ||
- **Support Vector Machine (SVM)**: 90.92% | ||
- **Decision Tree**: 90.92% | ||
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Each model was evaluated based on its accuracy in predicting sepsis surval using clinical records. | ||
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![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___21_0.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_1.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_11.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_13.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_15.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_17.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_19.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_3.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_5.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_7.png?raw=true) | ||
![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_9.png?raw=true) |
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