BioSignal-Smoking-Predictor-ML-AI

This study aims to harness AI and machine learning to create a robust predictive model using bio-signals, enabling the accurate identification of individuals as smokers or non-smokers.

1. INTRODUCTION

The objective of this study is to develop a machine learning model that utilizes bio-signals to accurately predict the smoking status of an individual. By leveraging AI and machine learning techniques, aim to provide a reliable tool for identifying individuals who are smokers or non-smokers. This predictive model can aid in the development of effective smoking cessation strategies and interventions, ultimately contributing to improved public health outcomes. The model will consider various bio-signals, such as physiological, behavioral, or environmental factors, to achieve accurate and reliable predictions. The goal is to create a practical and accessible solution that can assist healthcare professionals in assessing an individual's smoking status and provide personalized recommendations for smoking cessation.

2. TABLE OF CONTENTS

• DATA PREPROCESSING
• DATA EXPLORATION
• FEATURE SELECTION
• TRAINING
• EVALUATION
• PARAMETER TUNING
• CONCLUSIONS
• REFERENCES