Application And Research Of Decision Tree In Medical Diagnosis

Data mining is the most signifieant field of information teehnology, which is combined with many theory and technology such as database, artificial intelligence, machine learning and statisties. As one of the most widely used classification algoritms of data mining, decision tree algorithm is characterized by high accuracy, simplicity, high efficiency, and can be translated to rules easily. This paper uses decision tree algorithm as the data mining algorithm for mining medical diagnosis data. The decision tree may be very huge when the decision tree was first generated, and the corresponding rules will be very hard to understand. So it needs pruning algorithm to reduce the scale of the decision tree. So pruning algorithm is very important in decision tree algorithms. This paper focuses on the application of data mining in medical diagnosis, and analyses the advantage and disadvantage of classic decision tree pruning algorithms, presents a new pruning algorithm based on multi-strategy. This algorithm considers user's needs, and can accepts descriptive parameters of decision trees on different data mining set. Finally, it gets the ideal decision tree model. The results of experiments show that this algorithm can balances the precision and complexity better, and meets the needs of medical diagnosis in different application context, and ensures better adaptability in different data mining set.