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  Published Paper Details:

  Paper Title

AI Based Disease Prediction Technology (DPT-AI-CH2)

  Authors

  Buddh Bhagwan Sahu

  Keywords

Prediction, Personalization, Participation, Database, Non-communicable chronic diseases, Proactive, Healthcare innovation and Prevention, Dataset. Linear, Non-Linear learning techniques, support vector machines, Sigmoid and Restricted Boltzmann Methods.

  Abstract


Learning Prediction Technique and Data Science (LPT-DS) Is one of the way to simulate disease prediction technique. When we organize the data between method and machine will fetch the data from metadata. Machine always gives more accurate results based on the collected data from metadata. All metadata will follow to the linked and trained data. The dataset is a collection of metadata with the combination of dataset and trained data will gives more desirable results including learning techniques i.e. Regression, Clustering, Time-Series Forecasting, Sigmoid model, and Support Vector Machine. When past life stories appear, the metadata has been referred to as past life regression. However, regression technique does not try and prove the truth of these stories; rather, it uses the client's interpretation as a trained list, it means of providing a powerful transpersonal experience for execution. When traumatic memories, brain or mental health based on the source of the problem, regression technique includes.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT24A3080

  Paper ID - 252917

  Page Number(s) - j106-j114

  Pubished in - Volume 12 | Issue 3 | March 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Buddh Bhagwan Sahu,   "AI Based Disease Prediction Technology (DPT-AI-CH2)", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 3, pp.j106-j114, March 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A3080.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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