ENHANCING FILTER BASED ALGORITHMS FOR SELECTING OPTIMAL FEATURES FROM THYROID DISEASE DATASET
K. Pavya · International Journal of Advanced Research in Computer Science · 2017
In medical science, automatic disease diagnosis is an invaluable tool because of restricted observation of the specialist and uncertainties in medical knowledge. Advances in medical information technology have enabled healthcare industries to automatically collect huge amount of data through clinical laboratory examinations. To explore these data, the past few years have envisaged the use of Computer Aided Diagnosis (CAD) systems in many screening sites and hospitals. While using CAD, thyroid function diagnosis is considered as a classification problem, which can automatically identify the type of thyroid (hyper, hypo or normal). Machine learning techniques are increasingly introduced to construct the CAD systems owing to its strong capability of extracting complex relationships in the biomedical data.