Predicting Liver Disease using Binary Classification

About this Dataset

Context

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 the diagnosis.

Content

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)

Heatmap showing the correlation

Pairplot

Acknowledgements

This dataset was downloaded from the UCI ML Repository:

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Inspiration

Use these patient records to determine which patients have liver disease and which ones do not.

Analytics

Complete coding with the explanation and result is uploaded in the Github.

https://github.com/Mchockalingam/OpenDL/blob/master/Analysing%20Diabetes