Main Reference PaperComparison of Different Machine Learning Approaches to Predict Small for Gestational Age Infants, IEEE Transactions on Big Data, 2018[Python/Hadoop]
  • The proposed work applies the Machine Learning algorithms to predict SGA(small for gestational age) before birth and compare the various machine learning algorithm results. To evaluates the the prediction performance of Machine Learning algorithms are support vector machine, random forest, logistic regression and sparse logistic regression.

Description
  • The proposed work applies the Machine Learning algorithms to predict SGA(small for gestational age) before birth and compare the various machine learning algorithm results. To evaluates the the prediction performance of Machine Learning algorithms are support vector machine, random forest, logistic regression and sparse logistic regression.

  • To develop effective SGA prediction models.

  • To improve the classification accuracy.

Aim & Objectives
  • To develop effective SGA prediction models.

  • To improve the classification accuracy.

  • Effective missing data prediction mechanism is contributed.

Contribution
  • Effective missing data prediction mechanism is contributed.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

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