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A model predicting hospital admission based on from the emergency department

An IEEE paper is attached which has to be implemented.

DESCRIPTION:

Developing a model for the Prediction of Hospital admission from the emergency department.

Practical Implementation of the models of different machine learning algorithm like Logistic Regression,Decision Tree and Gradient Boosted Machine Algorithms and comparing there performance based on Accuracy and AUC-ROC curve.

All these algorithms are implemented in the routinely collected administrative data from two major acute hospitals of Northern Ireland for predicting the risk of admission from the Emergency Department.

The method for this study involved seven data mining tasks.

These were:

1. Data extraction;

2. Data cleansing and feature

engineering;

3. Data visualisation and descriptive statistics;

4. Data splitting into training (80%) and test sets (20%);

5. Model tuning using the training set and 10-fold cross validation

repeated 5 times;

6. Predicting admissions based on

the test data set and;

7. The evaluation of model performance

based on predictions made on the test data.

These steps help to

ensure the models are optimal and prevent against overfitting

The Enhancement Part is Designing the new algorithm which will give a better performance in terms of Accuracy and AUC-ROC curve than all the three algorithms i.e. Logistic Regression,Decision Tree and Gradient Boosted Machine algorithm.

Write a thesis and a new research paper.

Link for the paper to implement.

[login to view URL]

Skills: Big Data Sales, Data Mining, Data Science, Machine Learning, Python

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