Concept Drift in Surgery Prediction
Independent thesis Advanced level (degree of Master (One Year))Student thesis
Context: In healthcare, the decision of patient referral evolves through time because of changes in scientific developments, and clinical practices. Existing decision support systems of patient referral are based on the expert systems approach. This usually requires manual updates when changes in clinical practices occur. Automatically updating the decision support system by identifying and handling so-called concept drift improves the efficiency of healthcare systems. In the stateof-the- art, there are only specific ways of handling concept drift; developing a more generic technique which works regardless of restrictions on how slow, fast, sudden, gradual, local, global, cyclical, noisy or otherwise changes in internal distribution, is still a challenge. Objectives: An algorithm that handles concept drift in surgery prediction is investigated. Concept drift detection techniques are evaluated to find out a suitable detection technique in the context of surgery prediction. Moreover, a plausible combination of detection and handling algorithms including the proposed algorithm, Trigger Based Ensemble (TBE)+, are evaluated on hospital data. Method: Experiments are conducted to investigates the impact of concept drift on prediction performance and to reduce concept drift impact. The experiments compare three existing methods (AWE, Active Classifier, Learn++) and the proposed algorithm, Trigger Based Ensemble(TBE). Real-world dataset from orthopedics department of Belkinge hospital and other domain dataset are used in the experiment. Results: The negative impact of concept drift in surgery prediction is investigated. The relationship between temporal changes in data distribution and surgery prediction concept drift is identified. Furthermore, the proposed algorithm is evaluated and compared with existing handling approaches. Conclusion: The proposed algorithm, Trigger Based Ensemble (TBE), is capable of detecting the occurrences of concept drifts and to adapt quickly to various changes. The Trigger Based Ensemble algorithm performed comparatively better or sometimes similar to the existing concept drift handling algorithms in the absence of noise. Moreover, the performance of Trigger Based Ensemble is consistent for small and large dataset. The research is of twofold contributions, in that it is improving surgery prediction performance as well as contributing one competitive concept drift handling algorithm to the area of computer science.
Place, publisher, year, edition, pages
2012. , 69 p.
Concept drift, Concept Drift in Surgery Prediction, Concept Drift Handling Algorithm, Trigger Based Ensemble
IdentifiersURN: urn:nbn:se:bth-2330Local ID: oai:bth.se:arkivex24D59D68FDF3C603C1257B1800252E0DOAI: oai:DiVA.org:bth-2330DiVA: diva2:829599
Persson, MarieLavesson, Niklas