Proactive Prediction of Air Quality Index using Machine Learning Techniques to Detect Lung Cancer

Abstract

Air pollution play a critical position when it comes to the effect, it has on the surroundings in flip affecting public health, socio-economics, politics and agriculture. In the usage of a system mastering algorithms, we record the pollutant concentrations using air high-quality index (API) in India over the term of (January 1970 – January 2015). Delhi is one of the most polluted towns globally, particularly due to vehicle pollution. Factors that implement the machine learning algorithm at the input are meteorological parameters, pollutant concentrations and timestamp. This paper proposes a machine learning technique to predict lung cancer due to air pollution with determination of preventing health concerns, like respiratory infections, asthma, pneumonia, cardiovascular problems and lung cancers. The proposed techniques can be used by the health department of urban to check air quality, physicians to estimate spatial–temporal profile of air pollution and air excellent indices. Further research is to study the efficiency and potency of ML with geometric, computational and statistical models.