Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model

B. Trstenjak, D. Donko, Z. Avdagic


Nowadays, we are witnessing the rapid development of medicine and various methods that are used for early detection of diseases. In order to make quality decisions in diagnosis and prevention of disease, various decision support systems based on machine learning methods have been introduced in the medical domain. Such systems play an increasingly important role in medical practice. This paper presents a new web framework concept for disease prediction. The proposed framework is object-oriented and enables online prediction of various diseases. The framework enables online creation of different autonomous prediction models depending on the characteristics of diseases. Prediction process in the framework is based on a hybrid Case Based Reasoning classifier. The framework was evaluated on disease datasets from public repositories. Experimental evaluation shows that the proposed framework achieved high diagnosis accuracy.


disease prediction; web framework; hybrid model; Case Based Reasoning

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