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The empirical pulmonology study of a representative sample (n\u2009=\u2009132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease\u2019s case, Random Forest classifier outperforms other techniques with 97.7\u2009per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. 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