Smart Detection of Passive Smokers using Machine Learning - Shah, Pratham; Parekh, Astha; Bajaj, Divyansh; Swain, Debabrata; Kumar, Manish; Bhilare, Amol (International Conference on Computing Communication Control and Automation (Online), 2024)
Smoking is a destructive and addictive practice of inhaling burning plant material. It produces nicotine in the bloodstream that creates a negative impact on the bones, hormones, DNA, eyes, and the immune system. This has become the source of a great concern, especially amongst the young generation. Machine Learning is used to find a pattern of biological attributes to identify possible smokers. The process of teaching a machine to do intelligent classification from a sizable archive of historical data is known as machine learning. The aim of this paper is to develop a learning model that can detect passive smokers through features like eyesight, weight, fasting blood sugar, cholesterol and more. On this dataset, a number of data preprocessing operations are carried out to build a reliable model which include checking for null values , encoding the data , balancing the dataset and scaling it and selection of highly correlated features. A variety of supervised learning models are employed in the model training process to create a robust machine learning model. From the set of models applied, K-Nearest Neighbour (KNN) (84 %) and Random Forest (RF) (86 %) gave the highest accuracy. However, implementing Random Forest with Hyperparameter tuning using GridSearchCV resulted in a higher accuracy (87 %).