A forensic autopsy is a surgical process in which experts collect a deceased body's internal and external information. These death certificates are the source of timely warnings of an increase in disease activity. It's only helpful if accurate and quantitative data is available. Therefore, the Classification of plain text medical autopsy reports reduces the time consumption and irregularities. The motive is to design an automatic text classification system that classifies plain text autopsy reports. Therefore, a methodology proposes using different Automatic Text Classification Techniques (ATC). This technique has embedded Feature Extraction, Feature Representation, and Feature Reduction techniques. These techniques use for the construction of classification models that classify the text of autopsy reports. Data sets collected from these types will be helpful in future experiments. Finally, the performance of the classifier measures by using different Evaluation parameters. These Evaluation Measures are Precision, Recall, Accuracy, and F-measure.
|Title of host publication||2022 2nd International Conference on Computing and Information Technology (ICCIT)|
|Number of pages||153|
|Publication status||Published - 17 Feb 2022|
|Event||International Conference on Computer and Information Technology (ICCIT) - Tabuk, Saudi Arabia, Tabuk, Saudi Arabia|
Duration: 25 Jan 2022 → 27 Jan 2022
|Conference||International Conference on Computer and Information Technology (ICCIT)|
|Period||25/01/22 → 27/01/22|