Published: 28 July 2023| Version 1 | DOI: 10.17632/vbvw7y6stc.1
Elsayed Elmandoh


this is my dataset test value lowest_normal_range highest_normal_range result ALT (GPT) 8.60 0.0 31.0 0 AST (GOT) 14.80 0.0 31.0 0 Calcium Phosphonazo 9.64 8.6 10.3 0 Creatinine 0.83 0.6 1.1 0 C-Reactive Protein 42.98 0.0 5.0 1 It appears that you have a dataset with medical test results for various parameters. Each row represents a specific test, and the columns provide information about the test, its value, the lowest normal range, the highest normal range, and the result. Here's a breakdown of the columns: 1. `test`: This column contains the name of the medical test, such as ALT (GPT), AST (GOT), Calcium Phosphonazo, Creatinine, and C-Reactive Protein. 2. `value`: This column represents the actual value obtained from the medical test. 3. `lowest_normal_range`: This column indicates the lowest value within the normal range for that specific test. 4. `highest_normal_range`: This column indicates the highest value within the normal range for that specific test. 5. `result`: This column contains the result of the test, typically denoted as 0 or 1. It's likely that 0 indicates a normal result, and 1 indicates an abnormal or elevated result. Using this dataset, you can perform various analyses and insights related to the medical test results. Some potential analyses include: 1. **Identifying Abnormal Results:** You can filter the dataset to identify tests with a result of 1, indicating abnormal values outside the normal range. This can help in the early detection of health issues. 2. **Range Comparison:** By comparing the `value` column with the `lowest_normal_range` and `highest_normal_range`, you can determine if a specific test result falls within the normal range or if it is below or above the normal limits. 3. **Statistics and Visualization:** You can calculate statistical measures like mean, median, standard deviation, etc., for each test and visualize the data through plots or charts to gain insights into the distribution of test results. 4. **Correlations:** You can explore possible correlations between different test results to understand how they might be related in the context of medical conditions. 5. **Alerting System:** If this dataset is being used in a real-time healthcare setting, you can set up an alerting system to notify healthcare professionals when certain test results exceed the normal range, enabling timely intervention. Remember that medical data handling must be done with care, ensuring patient privacy and adhering to ethical and legal guidelines, especially if this dataset contains sensitive patient information. Additionally, it's essential to involve qualified healthcare professionals in the interpretation of the results and any decisions based on the data.



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