Predictive Disease Data Analysis of Air Pollution Using Supervised Learning

Authors

  • Manikanta Sirigineedi  Department of Information Technology, Vishnu Institute of Technology, Bhimavaram, Andhra Pradesh, India
  • Padma Bellapukonda  Department of Information Technology, Shri Vishnu Engineering College for Women, Bhimavaram, Andhra Pradesh, India
  • R N V Jagan Mohan  Department of Computer Science and Engineering, SRKR Engineering College, Bhimavaram, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT2283118

Keywords:

Air pollution, supervised machine learning, carbon dioxide, cardiovascular, breathing, asthma, Radon Gas.

Abstract

Air pollution is a combination of natural and manmade substances in the air we breathe. It is classified into two major categories, i.e. outdoor air pollution and indoor air pollution. Outdoor air pollution involves exposures that take place outside the built environment where as, indoor air pollution involves exposure to particulates, carbon oxides, and other pollutants carried by indoor air or dust. In this paper, we would like to propose that air pollution relates to increased cardiovascular and breathing related problems data rate, prediction with supervised machine learning. The study is largest of its benevolent to investigate the short-term impacts of air pollution is conducted completed a 30-years epoch. This study analyzes the experiments data on air pollution and humanity in India and other regions. The experimental result is on Risk of Cardiovascular Illness in several patients data classification is used.

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Published

2022-07-30

Issue

Section

Research Articles

How to Cite

[1]
Manikanta Sirigineedi, Padma Bellapukonda, R N V Jagan Mohan, " Predictive Disease Data Analysis of Air Pollution Using Supervised Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.105-110, July-August-2022. Available at doi : https://doi.org/10.32628/CSEIT2283118