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              <text>A study on prediction of health care data using machine learning</text>
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              <text>Classification; Clustering; Data mining; Electronic-Health Record(HER); Health care; Health informatics; Machine Learning</text>
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              <text>Every clinical-decision relies on the doctors experience and knowledge. Perhaps this conventional practice may look appropriate, but it may lead to unpredictable errors, biases, and maximized costs that may affect QoS (Quality-of-Service) given to patients. To help the doctor to save time, the conventional practice to analyze the data for clinical-decision support has to be updated. Machine Learning (ML) and Data Mining (DM) algorithms have applied to have greater and higher predictions. This paper studies a set of ML algorithms by which clinical-predictions are going to be more appropriate and cost-effective.  IJSTR 2020.</text>
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              <text>Balamurugan M.; Krishna A.V.N.; Balachandran K.; Bhuvana J.</text>
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              <text>International Journal of Scientific and Technology Research, Vol-9, No. 4, pp. 616-620.</text>
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              <text>ISSN: 22778616</text>
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              <text>Balamurugan M., Department of CSE, CHRIST (Deemed to be University), Bengaluru, India; Krishna A.V.N., Department of CSE, CHRIST (Deemed to be University), Bengaluru, India; Balachandran K., Department of CSE, CHRIST (Deemed to be University), Bengaluru, India; Bhuvana J., Department of MCA, Jain (Deemed to be University), Bengaluru, India</text>
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