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Smoking has been proven to negatively affect your health in a multitude of ways. As of 2009, smoking is the leading cause of preventable morbidity and mortality in the United States, continuing to plague the country's overall health. This study aims to investigate the viability and effectiveness of machine learning algorithms for predicting the smoking status of patients based on their blood tests and vital readings results. The analysis of this study is divided into two parts: In part 1, we use One-way ANOVA analysis with SAS to show the statistically significant difference in blood test readings between smokers and nonsmokers. The results show that the difference in INR, which measures the effectiveness of anticoagulants, was significant in favor of nonsmokers which further confirms the health risks associated with smoking. In part 2, we use five machine learning algorithms: Naive Bayes, MLP, Logistic, J48 and Decision Table to predict the smoking status of patients. To compare the effectiveness of these algorithms we use: Precision, Recall, F-measure and Accuracy measures. The results show that Logistic algorithm outperformed the four other algorithms with Precision, Recall, F-Measure, and Accuracy of 83%, 83.4%, 83.2%, 83.44%, respectively.

Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis