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In the Materials and Methods section, we utilized a dataset from DACON, encompassing information from 549 patients and 28 features. Data preparation involved addressing categorical and non-categorical values, null values, and outliers, ensuring dataset cleanliness by eliminating outliers and null values. We applied one-hot encoding and label encoding for categorical and non-categorical values, respectively, and converted raw data into a suitable format for machine learning models. Tree feature selection was employed to identify 21 pertinent attributes, and we implemented three state-of-the-art machine learning models using the refined dataset. This meticulous approach in data preparation lays the foundation for accurate model training and evaluation. |
IEEE/ICACT20240384 Slide.08
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