Disease Prediction in Health Care System

Abstract : To keep pace with the developments in medical informatics, health medical data is being collected continually. But, owing to the diversity of its categories and sources, medical data has become so complicated in many hospitals needs a clinical decision support (CDS) system for its management. To effectively utilize the accumulating health data, we propose a CDS framework that can integrate heterogeneous health data from different sources such as laboratory test results, basic information of patients, and health records into a consolidated representation of features of all patients. Using the electronic health medical data so created, multilevel classi?cation was employed to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients’ health issues more efficiently. Once the physician diagnoses the disease of a patient, the next step is to consider the likely complications of that disease, which can lead to more diseases. Previous studies reveal that correlations do exist among some diseases. Considering these correlations, a k-nearest neighbors algorithm is improved for multilevel learning by using correlations among labels (CML-kNN). The CML-kNN algorithm ?rst exploits the dependence between every two labels to update the origin label matrix and then performs multilevel learning to estimate the probabilities of labels by using the integrated features. Finally, it recommends the top N diseases to the physicians. Experimental results on real health medical data establish the effectiveness and practicability of the proposed CDS framework.

 EXISTING SYSTEM :

  The existing clinical decision support systems are poor in processing large volumes of multi-structured healthcare data and in providing accurate health recommendation, in practice

 DISADVANTAGE :

 Problem transformation method ?rst transforms one multilevel dataset into multiple single-label datasets, and then exploits existing single-label learning algorithm to process each single-label dataset

 PROPOSED SYSTEM :

 Using Pattern Matching Algorithm the laboratory test data and basic information of patients, a set of experiments of different multi-label learning methods were performed to confirm the effectiveness and practicality of the proposed framework. The Disease Prediction in health care system is an information system that offers knowledge and personalized information to users in enhancing health and healthcare outcomes.

 ADVANTAGE :

 • A novel framework is proposed for retrieving the most relevant information of patients from multiple data sources,. • Such as laboratory test data, basic information of patients, symptoms of patients and electrocardiogram data, and for combining them to generate integrated features.

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