Currently submitted to: JMIR Medical Informatics (no longer under consideration since Jul 14, 2019)
Date Submitted: Jun 23, 2019
Open Peer Review Period: Jun 26, 2019 - Jul 14, 2019
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Risk factors identifying of lung cancer in the elderly: using deep learning method based on open survey data from 1996 to 2017
Lung cancer is one of the most dangerous malignant tumors to human health, which morbidity and mortality are growing fastest, especially in the elderly. With rapid growth of the elderly population in recent years, the lung cancer prevention and control is more and more serious.
The pathogenesis of lung cancer is a complex process involving a variety of risk factors. This study aims at identifying lung cancer risk factors of the elderly using deep learning method.
Based on open access data, we integrated multidisciplinary risk factors together, which included behavioral risk factors, disease history factors, environmental factors, demography factors and so on. We conducted data preprocessing work of these integrated data. Then deep neural network (DNN) models were trained of stratified elderly population. Risk factors of different stratification were extracted from these DNN models. Finally, quantitative analysis were conducted to identify risk factors of different groups.
The proposed method identified risk factors quantitatively of lung cancer incidence in different stratified elderly populations. Smoking was the leading cause of lung cancer incidence in the elderly. And men ≥65 years were more sensitive to smoking frequency than women. Cancer history played an important role in the incidence of lung cancer. Lung cancer incidence decreased more obviously in men than in women with stop smoking, especially non-small cell lung cancer (NSCLC) in elderly men.
This study demonstrates a risk factor identifying method of lung cancer incidence quantitatively in the elderly. We adopt deep neural network method in different stratified populations, which could reflect age and gender disparities. Our findings provide intervention indicators to prevent lung cancer in the elderly, especially in the older men.
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