Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors.
Source : https://www.kaggle.com/uciml/indian-liver-patient-records Dataset description : This data set contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The "Dataset" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). This data set contains 441 male patient records and 142 female patient records.
Any patient whose age exceeded 89 is listed as being of age "90".
Columns:
- Age of the patient
- Gender of the patient
- Total Bilirubin
- Direct Bilirubin
- Alkaline Phosphotase
- Alamine Aminotransferase
- Aspartate Aminotransferase
- Total Protiens
- Albumin
- Albumin and Globulin Ratio
- Dataset: field used to split the data into two sets (patient with liver disease, or no disease)
Built 7 classification models (Logistic Regression, Naive Bayes, MLP, SVC, KNN, Decision Tree, Random Forest, XGBoost) and evaluated their performances. Finally selected the Random Classifier as the final model since it had better performance in terms of Balanced Accuracy score, Recall and AUC.