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Get Diagnosis Of Hepatitis Using Decision Tree Algorithm
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How to fill out the Diagnosis of Hepatitis Using Decision Tree Algorithm online
This guide provides a clear and comprehensive overview of how to fill out the Diagnosis of Hepatitis form using the Decision Tree Algorithm online. By following these instructions, users can ensure accurate data input and streamline their process.
Follow the steps to accurately complete the Diagnosis of Hepatitis form online.
- Click the ‘Get Form’ button to obtain the Diagnosis of Hepatitis form and open it in your preferred online editor.
- Begin by reviewing the introductory section of the form, which provides essential information about hepatitis and the purpose of the form.
- Proceed to fill out the 'Age' field. Enter the patient's age from the options provided: 10, 20, 30, 40, 50, 60, 70, 80.
- Indicate the 'Sex' of the patient by selecting either 'Male' or 'Female' in the corresponding field.
- Complete the section for 'Steroid' use by selecting 'Yes' or 'No' based on the patient's medication history.
- Fill out the 'Antivirals' field in the same manner, choosing 'Yes' or 'No'.
- Continue to the symptomatic fields, including 'Fatigue', 'Malaise', 'Anorexia', 'Liver big', 'Liver firm', 'Spleen palpable', 'Spiders', 'Ascites', and 'Varices', selecting 'Yes' or 'No' for each.
- Enter the lab results for 'Vilirubin', 'ALK Phosphate', 'SGOT', 'Albumin', and 'PROTIME', choosing appropriate values from the range provided.
- In the 'HISTOLOGY' section, indicate whether the histology results are available by selecting 'Yes' or 'No'.
- Finally, review the 'Class' section where you will determine the patient's condition as either 'DIE' or 'LIVE' based on the analysis.
- Once you have filled out all the sections accurately, you can save changes, download, print, or share the form as needed.
Start filling out the Diagnosis of Hepatitis form online today to enhance your evaluation and diagnostics process.
A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes.
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