Diabetes, which is at the forefront of diseases of the age, is a disease that plays a leading role in the formation of many deadly diseases and is very common all over the world.
It is important to know whether a patient can be readmitted in a hospital. In this project, we tried predict whether diabetes patients will return to the hospital or not by using machine learning algorithms.
We used this dataset, Source of the data set: https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008
There are some general library requirements for the Project. The general requirements are as follows.
- Numpy
- Pandas
- Scikit-learn
For Visualization
- Matplotlib
- Seaborn
- Plotly
- Missingno
The library requirements specific to some methods are:
- Logistic Regression
- Gradient Boosting Classifier
- Random Forest Classsifer
- XGboost Classifier
- Light-GBM Classifier
- CatBoost Classifier
The data set represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria.
- It is an inpatient encounter (a hospital admission).
- It is a diabetic encounter, that is, one during which any kind of diabetes was entered to the system as a diagnosis.
- The length of stay was at least 1 day and at most 14 days.
- Laboratory tests were performed during the encounter.
- Medications were administered during the encounter. The data contains such attributes as patient number, race, gender, age, admission type, time in hospital, medical specialty of admitting physician, number of lab test performed, HbA1c test result, diagnosis, number of medication, diabetic medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization, etc."
The following steps were followed in this project:
- Exploratory data analysis
- Visualization
- Split data into training , validation and test set
- Feature Engineering
- Modelling
- Feature Importance
- Predict results
https://prezi.com/view/TJ6WPvqUV5962zIIeEnV/
| Name |
|---|
| Ayse Nur TURKASLAN |
| H.Kubra KUCUKKARTAL |