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Sepsis survival prediction
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abhisheks008 authored Jul 17, 2024
2 parents ca8c06c + 13f56b9 commit 95e4b77
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110,205 changes: 110,205 additions & 0 deletions Sepsis Survival Prediction/Dataset/s41598-020-73558-3_sepsis_survival_primary_cohort.csv

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70 changes: 70 additions & 0 deletions Sepsis Survival Prediction/Model/README.md
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# Sepsis Survival Prediction - Models

## Models Implemented

### 1. Random Forest

**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.

### 2. XGBoost

**Description**: XGBoost is an optimized gradient boosting framework that is highly efficient and scalable, often used for structured data.

### 3. Logistic Regression

**Description**: Logistic Regression is a linear model that uses the logistic function to model the probability of a binary class.

### 4. Gradient Boosting

**Description**: Gradient Boosting builds models sequentially, with each new model correcting errors made by the previous ones.

### 5. AdaBoost

**Description**: AdaBoost is an ensemble learning method that combines multiple weak classifiers to create a strong classifier.

### 6. CatBoost

**Description**: CatBoost is a gradient boosting algorithm that handles categorical features automatically and efficiently.

### 7. LightGBM

**Description**: LightGBM is a gradient boosting framework designed for efficiency with large datasets and low memory usage.

### 8. K-Nearest Neighbors (KNN)

**Description**: K-Nearest Neighbors is a simple, instance-based learning algorithm where classification is based on the majority vote of the nearest neighbors.

### 9. Support Vector Machine (SVM)

**Description**: SVM is a supervised learning model that finds the hyperplane which best divides a dataset into classes.

### 10. Decision Tree

**Description**: Decision Trees are non-parametric supervised learning methods used for classification based on simple decision rules inferred from the data features.

## Performance of the Models based on the Accuracy Scores

- **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%

Each model was evaluated based on its accuracy in predicting sepsis surval using clinical records.

![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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