Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario.

Journal: SLAS technology
PMID:

Abstract

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing. A three-stage AI-based data generating strategy is suggested to handle missing datasets, using a small sample dataset obtained from a smart healthcare program community in a specific city: Step one involves generating the dataset's basic attributes using a tree-based generation strategy that takes the original data distribution into account. Step two involves using the Naive Bayes algorithm to create basic indicators of behavioural capability assessment for the samples. Step three builds on stage two and uses a multivariate linear regression method to create evaluation criteria and indicators of high-level behavioural capability. Six problems involving multiple classifications and two tasks using multiple labels are implemented using various neural network-based training strategies on the obtained data to assess the usefulness of the dataset for downstream tasks. To ensure that the data collected is genuine and useful, the experimental data must be analysed and expert knowledge must be included.

Authors

  • B RaviKrishna
    Department of Artificial Intelligence and Data Science, Vignan Institute of Technology & Science, Hyderabad, India. Electronic address: ravikrishnabh@gmail.com.
  • Mohammed E Seno
    Department of computer science, Al-Maarif university college, Al Anber 31001, Iraq.
  • Mohan Raparthi
    Software Engineer, alphabet Life Science, Dallas Texas, 75063, US.
  • Ramswaroop Reddy Yellu
    Independent Research, Richmond VA 23233, University of Texas, Austin, USA.
  • Shtwai Alsubai
    Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
  • Ashit Kumar Dutta
    Department of Computer Science and Information Systems, College of Applied Sciences, Almaarefa University, Riyadh 11597, Saudi Arabia.
  • Abdul Aziz
    Department of Software Engineering, National University of Computer and Emerging Sciences, Karachi, Pakistan.
  • Dilora Abdurakhimova
    Department of Corporate Finance and Securities, Tashkent State University of Economics, Tashkent, Uzbekistan.
  • Jyoti Bhola
    Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. Electronic address: jyotib14041992@gmail.com.