Discriminant analysis on influencing factors of hope level in patients with lung cancer based on decision tree and Logistic regression models

doi:10.16352/j.issn.1001-6325.2026.03.0402

Abstract

Abstract: Objective To identify influencing factors of hope level in patients with lung cancer, so as to find and identify patients with lower hope level as early as possible, and to provide reference for the formulation of intervention measures to improve hope level in patients with lung cancer. Methods Total of 266 patients with lung cancer who were treated in Peking Union Medical College Hospital from July 2024 to December 2024 were selected as the research objects. Herth Hope Index (HHI) was used to investigate the hope level of the included patients.The hope level of lung cancer patients with different characteristics was compared, and the decision tree(DT) and Logistic regression model were used to analyze the influencing factors of the hope level of lung cancer patients. Results After excluding 9 patients who answered invalid questionnaires, 257 patients were included in this study. Logistic regression model showed that the days since diagnosis (B=-0.001), lung cancer classification[adenocarcinoma(B=0.177), squamous cell carcinoma (B=1.949), others (B=1.458)], education level [high school, vocational school or technical school (B=-1.182), junior college (B=0.392), bachelor′s degree or above (B=1.040)], immunotherapy(B=-0.682), the score of Eastern Cooperative Oncology Group (ECOG) performance status (PS) [grade 1 (B=1.657), grade 2 (B=2.102) , grade 3 (B=0.171)]and the score of Connor-Davidson Resilience Scale (CD-RISC) (B=-0.060) were independent factors influencing the occurrence of the medium hope level in lung cancer patients.The results of decision tree showed that the total score of CD-RISC was the main factor affecting the hope level of lung cancer patients, followed by education level and the days of diagnosis. The area under the curve(AUC) of the two models was 0.853(0.806,0.900) and 0.850 (0.801, 0.898) respectively with no statistically significant difference (Z=0.144, P=0.885). Conclusions Both Logistic regression model and decision tree have certain application value in predicting the hope level of lung cancer patients. It is suggested to combine the two models in clinical practice in order to improve the hope level of lung cancer patients.

Key words: hope level, psychological resilience, Logistic regression, decision tree

CLC Number:

R734.2

LYU Juan, JI Shuo, LIU Ya, SUN Wen, PAN Ruili. Discriminant analysis on influencing factors of hope level in patients with lung cancer based on decision tree and Logistic regression models[J]. Basic & Clinical Medicine, 2026, 46(3): 402-410.

References

[1] Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. Ca-Cancer J Clin, 2021,71:209-249.doi:10.3322/caac.21660.
[2] Liu X, Yu Y, Wang M, et al. The mortality of lung cancer attributable to smoking among adults in China and the United States during 1990–2017[J]. Cancer Commun, 2020,40:611-619.doi:10.1002/cac2.12099.
[3] Osowiecka K, Kieszkowska-Grudny A, Środa R, et al. Identification of cognitive strategies used by cancer patients as a basis for psychological self-support during oncological therapy[J]. Int J Env Res Pub He, 2022,19:9243.doi:10.3390/ijerph19159243.
[4] McLouth LE, Shelton B , Bursac V, et al. “Pathways”: a hope-enhancing intervention for patients undergoing treatment for advanced lung cancer[J]. Psycho-Oncology, 2024,33:e6316.doi:10.1002/pon.6316.
[5] Huang M, Hung Y, Lee W, et al. Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis[J]. J Med Syst, 2012,36:407-414.doi:10.1007/s10916-010-9485-0.
[6] Herth K. Abbreviated instrument to measure hope: development and psychometric evaluation[J]. J Adv Nurs, 1992,17:1251-1259.doi:10.1111/j.1365-2648.1992.tb01843.x.
[7] Xu R, Chen J, Chen D, et al. CT-guided percutaneous microwave ablation combined with local radiotherapy or chemotherapy of malignant pulmonary tumors[J]. Curr Radiopharm, 2024,17:184-199.doi:10.2174/0118744710261655231214105406.
[8] Yu X, Zhang J, Yu XN, et al. Factor analysis and psychometric evaluation of the Connor-Davidson resilience scale (CD-RISC) with Chinese people[J]. Soc Behav Personal, 2007,35:19-30.doi:10.2224/SBP.2007.35.1.19.
[9] Cheng L, Gao W, Xu Y, et al. Anxiety and depression in rheumatoid arthritis patients: prevalence, risk factors and consistency between the hospital anxiety and depression scale and Zung′s self-rating anxiety scale/depression scale[J]. Rheumatol Adv Pract, 2023,7:rkad100.doi:10.1093/rap/rkad100.
[10] Bando T, Onishi C, Imai Y. Treatment-associated symptoms and coping of postoperative patients with lung cancer in Japan: development of a model of factors influencing hope[J]. Jpn J Nurs Sci, 2018,15:237-248.doi:10.1111/jjns.12193.
[11] Gao Y, Yuan L, Pan B, et al. Resilience and associated factors among Chinese patients diagnosed with oral cancer[J]. Bmc Cancer, 2019,19:447.doi:10.1186/s12885-019-5679-0.
[12] 霍艳飞. 肺癌化疗患者希望水平现状及影响因素研究[J]. 齐齐哈尔医学院学报, 2018,39:2462-2464.doi:10.3969/j.issn.1002-1256.2018.20.044.
[13] 朱文曦. 胃肠道癌症患者希望水平与心理弹性的相关性研究[J]. 当代护士, 2021,28:44-47.doi:10.19791/j.cnki.1006-6411.2021.28.015.
[14] 王艳华, 阎成美, 黄丽婷, 等. 乳腺癌住院患者疾病不确定感与希望的相关性研究[J]. 护理学报, 2007,14:15-17.doi:10.3969/j.issn.1008-9969.2007.01.007.
[15] Yang Y, Liu L, Wang X, et al. Prevalence and associated positive psychological variables of depression and anxiety among Chinese cervical cancer patients: a cross-sectional study[J]. PLoS One, 2014,9:e94804.doi:10.1371/journal.pone.0094804.