Diagnosis of liver failure using flask web framework

The liver cleans the body’s blood which converts nutrients and drugs into active molecules. Liver removes poisons and chemical waste from the blood for elimination. Untreated liver failure may kill. Machine learning predicts early sickness in contemporary healthcare. A machine learning model is constructed utilizing liver failure data. Univariate and multivariate analyses, along with any other required preprocessing procedures are put into place. Univariate analysis is useful for making changes to file names, adding new data, removing old data, and defining data types. Pairplot, heatmap, bar chart, and histogram are only some of the visualizations made possible by bivariate and multivariate data analysis for exploratory purposes. The data is presented so that the characteristics can be understood better, and a model is constructed using the random forest technique with oversampling to get the highest accuracy possible in predicting liver failure. Metrics like as accuracy, F1 score, recall, etc., are used to compare algorithms efficacy. Using the Flask web framework, the results of liver failure are obtained.