Description

The aim of this study is to extract relevant features from patient opinions in the medical domain using feature subset selection techniques. With the increasing availability of patient feedback in electronic health records, there is a need to efficiently process and analyze large volumes of unstructured text data. The proposed approach utilizes a bag of words model to represent patient opinions and employs sentiment analysis techniques to extract relevant features. The study employs machine learning and natural language processing techniques to identify the most informative features that can be used for classification, clustering, and regression tasks. Various feature selection techniques, such as information gain, chi-square, and mutual information, are explored to extract the most important features from the bag of words model. The study also investigates the effectiveness of different feature weighting methods, including TF-IDF and BM25. The extracted features are evaluated based on their accuracy, precision, recall, and F1-score, and their importance is analyzed for interpretability. The study explores various dimensionality reduction techniques, including principal component analysis and singular value decomposition, to reduce the feature space while preserving the relevant information. The proposed approach has potential applications in decision support systems for personalized medicine and patient-centered care. By efficiently extracting relevant features from patient opinions, healthcare providers can gain insights into patient needs and preferences, leading to improved patient outcomes and satisfaction.