Advanced Machine Learning for Disease Semantic Classification and Decision-Making

The classification of diseases is of utmost significance in the field of healthcare as it facilitates the process of diagnosing, treating, and conducting medical research. This paper proposes that illness categorization based on machine learning may be achieved by utilizing a dataset that encompasses several kinds of diseases, including diseases with IDs, names, types, classes, and associated features. The initial step in data pre-processing is the encoding of category variables and the normalization of numerical features. Various machine learning methods such as XGBoost, LGBM, Random Forest, and Extra Tree Classifier are employed to assess and categorize illness semantic types. The performance is assessed using evaluation criteria such as accuracy, precision, recall, and F1-score. Furthermore, hyperparameter optimization and cross-validation techniques are employed to optimize the performance and generalization capabilities of the model. A model called Ensemble Voting Classifier will be created to utilize the unique capabilities of each model in order to enhance the accuracy of classification. To enhance the accuracy and reliability of disease semantic typing, ensembles of diverse classifiers are constructed. The experimental findings demonstrate that the suggested technique is highly successful, achieving an accuracy of up to 82% in classifying illness semantic kinds. Additionally, the results provide valuable insights into the role of various variables used in the classification process.