INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Data mining is the process of looking at data from multiple perspectives and combining them with
desired data. It is about discovering knowledge or knowledge. Among the many software tools for
data analysis, data mining is the most widely used. This allows users to evaluate data from multiple
perspectives and dimensions, and group and save relationships. Technically, data mining can be
thought of as a step to follow in searching for patterns or analyzing relationships between different
sources in large datasets. Current developments in data mining and machine learning are improving
the conditions of primary health care by improving research in the field of biomedicine. Regular
recording is essential. New medical devices and technologies for diagnosis create mixed data and
big data. Therefore, to deal with this poor biomedical data, intelligent data mining and machine
learning methods are required to generate demand from the collected raw data calculated as medical
data mining. In medical records, medical records only look for patterns and associations that can
provide important information for an accurate diagnosis. This technology is used in many
medicines (medical applications) and helps to improve diagnosis. Accuracy of classification of
medical data and estimation of its value are the main tasks/challenges of medical data mining.
Better classifications are needed to improve the predictive value of additional clinical data, as
misclassifications can lead to poor estimates. When medical information is used only for medical
information, the basic and difficult problems are classification and prediction. Artificial neural
network (ANN) and logistic regression (LR) are often used to perform these functions. In our
presented research, a hybrid data mining model is proposed for classifying and estimating medical
data using LR and ANN, a cross-validated model (CVS) and a percentage selection method (FSM).
The performance of the proposed hybrid model will be evaluated based on classification accuracy.
Keywords:
Machine Learning, Logistic Regression, PIMA
Cite Article:
"Prediction of Diabetes Through Medical Dataset Using ML", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.b504-b510, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304172.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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