ABSTRACT
Web forums become the means of online communication and information sharing sources for the learning about health care and related treatment knowledge. By adopting web crawlers and natural language processing techniques, the automatic identification approach of the concerned HIV-related messages is proposed to facilitate the health authorities and social support groups in instant counseling. The proposed supervised GA/k-means for classification approach can help construct an effective identification and classification model with acceptable classification performance accompanied with its full flexibility to develop different fitness functions in accordance with the need of different requirements. Furthermore, with the aid of correspondence analysis, the most frequently used terms in concerned HIV-related messages are identified and focus on risky sexual behavior whereas unconcerned messages are those who of worried well.
Subject(s)
HIV Infections , Information Seeking Behavior , Internet , Self-Help Groups/statistics & numerical data , Social Support , Algorithms , Communication , Female , Humans , Information Dissemination , Information Services/statistics & numerical data , Male , Patient Education as Topic , Risk-Taking , Search Engine , Sexual BehaviorABSTRACT
Hypertension is a major disease, being one of the top ten causes of death in Taiwan. The exploration of three-dimensional (3-D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a prediction model for hypertension using anthropometric body surface scanning data. This research adopts classification trees to reveal the relationship between a subject's 3-D scanning data and hypertension disease using the hybrid of the association rule algorithm (ARA) and genetic algorithms (GAs) approach. The ARA is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. The proposed approach was experimented and compared with a regular genetic algorithm in predicting a subject's hypertension disease. Better computational efficiency and more accurate prediction results from the proposed approach are demonstrated.
