Sigara Kullanma Durumunun Çoklu Fizyolojik Ölçümler Ve Makine Öğrenmesi Teknikleri Kullanılarak Tahmini

Year 2021, Volume: 23 Issue: 67, 55 - 69, 15.01.2021

Aykut Eken Şevket Çalışkan Soner Çivilibal Pinar Deniz Tosun

https://doi.org/10.21205/deufmd.2021236705

Abstract

Sigara kullanımı toplumlarda gerek sağlık gerek ekonomik açıdan ciddi kayıplara sebep olmaktadır. Kullanım seviyesinin ölçümünde bir altın standart bulunmamasına rağmen, Fagerstörm Nikotin Bağımlılık Testi (Fagerstörm Test for Nicotine Dependency – FTND) ve HONC (Hooked on Nicotine Checklist) gibi geleneksel testler ve çeşitli nörogörüntüleme yaklaşımları kişinin sigara içme durumunun seviyesi hakkında bir bilgi vermektedir. Bu çalışmada, objektif bir veri olan fizyolojik parametrelerin subjektif bir veri olan bağımlılık testlerinin yerine kullanım seviye tespitinde yeni bir yaklaşım olarak kullanılabileceğini göstermek amaçlanmıştır. Bu amaçla çeşitli seviyelerdeki sigara kullanıcılarından fizyolojik sinyaller (elektrokardiyogram (EKG), Solunum ve Fotopletismografi) toplanmıştır. Bu sinyallerden elde edilen çeşitli öz niteliklerden makine öğrenmesi yaklaşımları kullanılarak katılımcılar düşük seviye veya yüksek seviye olarak tahmin edilmeye çalışılmıştır. Çalışma için önceden FTND bağımlılık testine giren değişik kullanım seviyelerinde 95 katılımcı alınıp bu kişilerden sırasıyla 50 saniyelik EKG, solunum ve fotopletismografi sinyalleri alınmıştır. Öznitelik çıkarımından sonra, parametre optimizasyonu ve sınıflandırma içeren 10 kat içiçe çapraz geçerlilik gerçekleştirilmiştir. Yapılan sınıflandırma sonucunda destek vektör makinesi kullanılarak %93, diskriminant analizi kullanılarak ise %91 doğruluk başarımı elde edilmiştir. Bu sonuçlar, yukarıda belirtilen fizyolojik parametrelerin makine öğrenmesi algoritmaları aracılığı ile sigara kullanım durumunun tespitinde kullanılabileceğini göstermektedir.

Keywords

Sigara Kullanımı, Fotopletismografi, EKG, Solunum, Makine Öğrenmesi, Sınıflandırma

Supporting Institution

Düzce Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

2018.06.07.810.

Thanks

Bu çalışma Düzce Üniversitesi Bilimsel Araştırma Projeleri Destek Programı kapsamında desteklenmiştir. (Proje Numarası: 2018.06.07.810.). Çalışmamıza desteklerinden dolayı Dr. Öğ. Ü. İkrime ORKAN UÇAR, Araş. Gör. Melahat Sevgül BAKAY ve Araş. Gör. Sümeyye ARIKAN’a teşekkür ederiz.

Prediction of smoking status by using multi-physiological measures and machine learning techniques

Abstract

Smoking causes severe economic and health losses in communities. Despite the lack of a gold standard for the measurement of usage level, conventional tests such as Fagerstörm Test for Nicotine Dependency (FTND), Hooked on Nicotine Checklist (HONC) and various neuroimaging approaches provide information about the level of smoking status. In this study, usage of objective physiological parameters was proposed as a new approach to detect level of status instead of subjective status tests. In order to achieve this physiological signals (i.e.., electrocardiogram (ECG), respiration and photoplestimography) were acquired from participants from different smoking status levels. Participants’ smoking status levels were predicted as high dependent and low dependent from features extracted from these physiological signals using machine learning approaches. For this study, 95 university students with different levels of smoking status were recruited according to FTND test results and ECG, respiration and photopletismography signals were acquired respectively for 50 seconds to provide data for machine learning models. After feature extraction, a 10 fold nested- cross validation that includes hyperparameter optimization and classification was performed. According to the classification results, 93 % accuracy and 91 % accuracy were found by using Support Vector Machine and Discriminant Analysis respectively. These results revealed that physiological parameters might be used to predict smoking status via machine learning algorithms.

Keywords

smoking status, photopletsmography, ECG, respiration, machine learning, classification

Project Number

2018.06.07.810.

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