BIOSIGNALS BASED SMOKING STATUS PREDICTION USING STANDARD AUTOENCODER AND ARTIFICIAL NEURAL NETWORK

Abstract

Smoking is still a major global health concern since it causes a host of illnesses and early deaths around the globe. Utilizing biosignals to predict smoking status can yield insightful information for tailored interventions and smoking cessation programs. This work presents a novel method that combines an artificial neural network (ANN) and a regular autoencoder to predict smoking status based on biosignals. The proposed method involves preprocessing biosignal data to extract relevant features, which are then input into an autoencoder for dimensionality reduction. The output of an autoencoder is used as input for predicting smoking status using an ANN. The model is trained and evaluated using a dataset containing biosignal data from individuals with a known smoking status. The suggested strategy is effective, as seen by the experimental results, which show a high degree of prediction accuracy about smoking status. The model's performance is further validated through comparisons with existing methods, showing superior performance in terms of accuracy and robustness. The developed model is integrated into a user-friendly application aimed at promoting smoking cessation. In addition to specific online pages aimed at enlightening users about the negative consequences of smoking and the advantages of stopping, the program offers users individualized insights into their smoking status based on biosignals. Additionally, a menu-based chatbot is included to address user queries and provide support for smoking cessation efforts. The implemented deep learning model achieves the desired level of accuracy in predicting smoker status, and the user-friendly application offers a convenient platform for public health and personalized healthcare interventions.