Improving Children Diagnostics by Efficient Multi-label Classification Method

Using intelligent computational methods may support children diagnostics process. As in many cases patients are affected by multiple illnesses, multi-perspective view on patient data is necessary to improve medical decision making. In the paper, multi-label classification method-Labels Chain is considered. It performs well when the number of attributes significantly exceeds the number of instances. The effectiveness of the method is checked by experiments conducted on real data. The obtained results are evaluated by using two metrics: Classification Accuracy and Hamming Loss, and compared to the effects of the most popular techniques: Binary Relevance and Label Power-set.