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decision tree model for predicting ovarian tumor malignancy based on clinical markers and preoperative circulating blood cells

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BMC MEDICAL INFORMATICS AND decision MAKING 2025年第1期25卷 1-11页

作者： Li, Yingjia Zhao, Xingping Zhou, Yanhua Gong, Lina Peng, Enuo Cent South Univ Xiangya Hosp 3 Changsha 410013 Peoples R China

ObjectiveOvarian cancer is a serious malignant tumor threatening women's health. The early diagnosis and effective treatments of ovarian cancer remain inadequate, and about 70% of ovarian cancers are in advanced stages when discovered. This study aimed to use the decision tree method of artificial intelligence machine learning to build a model for predicting the benign and malignant degree of ovarian cancer *** designA total of 758 patients were included in the study. These patients were diagnosed by B-ultrasound, CT or MR. The clinicopathological features and circulating blood cell indexes were recorded and analyzed. The prediction model of benign and malignant ovarian tumors was constructed by CART decision tree, and the receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of the decision tree *** was found that significant predictor variables included age, disease duration, patient general condition and menopausal status, ascites, tumor size, HE4, CA125, ROMA index, and blood routine related indicators (except for basophil count percentage and absolute value). In the constructed decision tree model, ROMA_after was the root node with the maximum information gain. ROMA_after, Mass size (MR/CT), HE4, CA125, platelet number, lymphocyte ratio, white blood cell count, post-menopause, hematocrit and mean platelet volume were important indicators in the decision tree model. The area under the receiver operating characteristic curve of this model for predicting benign and malignant ovarian cancer was *** decision tree model was successfully constructed based on clinical indicators and preoperative circulating blood cells, and showed better results in predicting benign and malignant ovarian cancer than alone imaging indicators or biomarkers among our data, which means that our model can more accurately predict benign and malignant ovarian cancer.

关键词： decision tree model Ovarian cancer Preoperative circulating blood cells Machine learning Prediction

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decision tree model to efficiently optimize the process conditions of carbonaceous mesophase prepared with coal tar

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CARBON LETTERS 2023年第2期33卷 419-429页

作者： Zhou, Chunru Wu, Peng Xu, Xinyuan Song, Weina Heilongjiang Univ Sci & Technol Coll Environm & Chem Engn Harbin 150022 Peoples R China

It is difficult to optimize the process parameters of directly preparing carbonaceous mesophase (CMs) by solvothermal method using coal tar as raw material. To solve this problem, a decision tree model for CMs preparation (DTC) was established based on the relationship between the process parameters and the yields of CMs. Then, the importance of variables in the preparation process for CMs was predicted, the relationship between experimental conditions and yields was revealed, and the preparation process conditions were also optimized by the DTC. The prediction results showed that the importance of the variables was raw material type, solvothermal temperature, solvothermal time, solvent amount, and additive type in order. And the optimized reaction conditions were as follows: coal tar was pretreated by decompress distillation and centrifugation, the solvent amount was 50.0 ml, the solvothermal temperature was 230 degrees C, and the reaction time was 5 h. These prediction results were consistent with the actual experimental results, and the error between the predicted yields and the actual yields was about - 1.1%. Furthermore, the prediction error of DTC method was within the acceptable range when the data sample sets were reduced to 100 sets. These results proved that the established DTC for chemical process optimization can effectively lessen the experimental workload and has high application value.

关键词： decision tree model Process optimization Carbonaceous mesophases Coal tar Machine learning technology

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Application of decision tree model in diagnosis of mycoplasma pneumoniae pneumonia with plastic bronchitis

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ITALIAN JOURNAL OF PEDIATRICS 2025年第1期51卷 1-7页

作者： Li, Lin Wang, Dong Yang, Rongrong Liao, Xing Wu, Ling Fujian Med Univ Coll Clin Med Obstet & Gynecol & Pediat Fujian Childrens HospDept Infect Dis Fujian BranchShanghai Childrens Med CtrNatl Reg Fuzhou 350014 Peoples R China

Background To establish a decision tree model of Mycoplasma pneumoniae pneumonia(MPP) complicated with plastic bronchitis(PB) in children, and to explore the application value of decision tree model in the auxiliary diagnosis of children. Methods A retrospective study was conducted to collect the clinical data of 214 children who met the admission criteria in Fujian Children's Hospital from June 2022 to June 2024, and they were divided into plastic bronchitis group (n = 66) and non-plastic bronchitis group (n = 148). Using R language, 70% of the data from each group of patients was randomly selected for training the model using decision tree algorithm analysis, thus generating a clinical diagnostic decision tree for Mycoplasma pneumoniae (MP) combined with PB. The generated decision tree model was validated on the validation sample dataset and the detection effect value of the model was calculated. Result In this study, a total of 22 indicators were employed to build the decision tree diagnostic model. Univariate statistical analysis was carried out prior to the model construction, and it was discovered that the differences of 13 indicators between the molded group and the non-molded group were statistically significant. A decision tree model with D-dimer >= 1.7ug/mL, C-reactive protein >= 15 mg/L, drug resistance or not, and serum ferritin<137 mg/L was constructed in the training sample dataset of the molded group and the non-molded group. The sensitivity of the decision tree model was 0.884, which was verified in the dataset of the remolded group and the non-molded group. The specificity was 0.727, and the area under the receiver operating characteristic curve was 0.831. Conclusion decision tree model can provide reference for the application of auxiliary diagnosis in children with mycoplasma pneumoniae pneumonia complicated with plastic bronchitis. The model has good discriminative ability in general, and is worthy of clinical application and further study.

关键词： decision tree model Mycoplasma pneumoniae Plastic bronchitis Childhood pneumonia Diagnosis

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Teicoplanin 24-h loading dose regimen using a decision tree model to target serum trough concentration of 15-30 μg/mL: A retrospective study

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JOURNAL OF INFECTION AND CHEMOTHERAPY 2025年第2期31卷 102564-102564页

作者： Kondo, Shoji Oda, Kazutaka Kaneko, Tetsuya Jono, Hirofumi Saito, Hideyuki Kumamoto Univ Hosp Dept Pharm 1-1-1HonjoChuo Ku Kumamoto Japan Kumamoto Univ Hosp Dept Infect Control 1-1-1 HonjoChuo Ku Kumamoto Japan Kumamoto Univ Dept Clin Pharmaceut Sci Grad Sch Pharmaceut Sci 5-1 OeChuo Ku Kumamoto Japan 1-1-1 Honjo Chuo Ku Kumamoto 8608556 Japan

Introduction: Teicoplanin (TEIC) is typically administered as a loading dose over 36-48 h. Achieving an effective concentration quickly is expected to treat severe infections, such as sepsis and methicillin-resistant Staphylococcus aureus infections. We aimed to identify the TEIC loading dose to be completed within 24 h, targeting the concentration of 15-30 mu g/mL and factors affecting the loading dose by utilizing the decision tree (DT) model. Methods: Patients treated with TEIC between January 2017 and December 2022 who met the 24-h loading dose regimen were enrolled. A LD22.5 (corrected TEIC loading dose targeting concentration of 22.5 mu g/mL) was determined, factors affecting the concentration were extracted, and a DT model was constructed. The validity of the DT was assessed using the coefficient of determination (R2) in the DT and population pharmacokinetics (PopPK) models for LD22.5. Result: A total of 149 patients were divided into training (n = 104, 70 %) and test groups (n = 45, 30 %). We indicated an average of 14.5 mg/kg for LD22.5 and extracted four factors (estimated glomerular filtration rate, age, albumin, and C-reactive protein) from the DT model. The R2 values were 0.724, 0.695, 0.681, and 0.653 for the DT models (training and test groups) and two PopPK models, respectively. Conclusion: We established a 24-h loading dose regimen targeting the TEIC concentration of 15-30 mu g/mL and identified four factors affecting the loading dose by using DT. By following the indicated DT algorithm flowchat, optimal decisions regarding the loading dose could be made for TEIC therapy.

关键词： Teicoplanin Therapeutic drug monitoring Loading dose decision tree model Retrospective study

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Factors associated with a better treatment efficacy among psoriasis patients: a study based on decision tree model and logistic regression in Shanghai, China

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BMC PUBLIC HEALTH 2024年第1期24卷 1页

作者： Shen, Fanlingzi Duan, Zhen Li, Siyuan Gao, Zhongzhi Zhang, Rui Gao, Xiangjin Li, Bin Wang, Ruiping Tongji Univ Shanghai Skin Dis Hosp Clin Res Ctr Sch Med 1278 Baode RdJingan Dist Shanghai 200443 Peoples R China Shanghai Univ Tradit Chinese Med Sch Publ Hlth 1200 Cai Lun Rd Shanghai Peoples R China

Background Many effective therapies for psoriasis are being applied in clinical practice in recent years, however, some patients still can't achieve satisfied effect even with biologics. Therefore, it is crucial to identify factors associated with the treatment efficacy among psoriasis patients. This study aims to explore factors influencing the treatment efficacy of psoriasis patients based on decision tree model and logistic regression. Methods We implemented an observational study and recruited 512 psoriasis patients in Shanghai Skin Diseases Hospital from 2021 to 2022. We used face-to-face questionnaire interview and physical examination to collect data. Influencing factors of treatment efficacy were analyzed by using logistic regression, and decision tree model based on the CART algorithm. The receiver operator curve (ROC) was plotted for model evaluation and the statistical significance was set at P < 0.05. Results The 512 patients were predominately males (72.1%), with a median age of 47.5 years. In this study, 245 patients achieved >= 75% improvement in psoriasis area and severity index (PASI) score in week 8 and was identified as treatment success (47.9%). Logistic regression analysis showed that patients with senior high school and above, without psoriasis family history, without tobacco smoking and alcohol drinking had higher percentage of treatment success in patients with psoriasis. The final decision tree model contained four layers with a total of seventeen nodes. Nine classification rules were extracted and five factors associated with treatment efficacy were screened, which indicated tobacco smoking was the most critical variable for treatment efficacy prediction. model evaluation by ROC showed that the area under curve (AUC) was 0.79 (95%CI: 0.75 similar to 0.83) both for logistic regression model (0.80 sensitivity and 0.69 specificity) and decision tree model (0.77 sensitivity and 0.73 specificity). Conclusion Psoriasis patients with higher educat

关键词： Psoriasis Treatment efficacy Tobacco smoking decision tree model Logistic regression

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Development and validation of diagnosis model for inflammatory bowel diseases based on a serologic biomarker panel: A decision tree model study

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ARAB JOURNAL OF GASTROENTEROLOGY 2025年第1期26卷 45-52页

作者： Zeng, Junxiang Huai, Manxiu Ge, Wensong Yang, Zhigang Pan, Xiujun Shanghai Jiao Tong Univ Xinhua Hosp Sch Med Dept Clin Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Xinhua Hosp Sch Med Dept Gastroenterol Shanghai Peoples R China Shanghai Jiao Tong Univ Xinhua Hosp Sch Med Dept Gastroenterol Surg Shanghai Peoples R China Shanghai Acad Expt Med Inst Artificial Intelligence Med Shanghai Peoples R China

Background and study aims: Currently, an increasing amount of experimental data is available on newly discovered biomarkers in inflammatory bowel diseases (IBD), but the role of these biomarkers is often questionable due to their limited sensitivity. Therefore, this study aimed to build a diagnostic tool incorporating a panel of serum biomarkers into a computational algorithm to identify patients with IBD and differentiate those with Crohn's disease (CD) from those with ulcerative colitis (UC). Patients and methods: We studied sera from 192 CD patients, 118 UC patients, 60 non-IBD controls and 60 healthy controls. Indirect immunofluorescence (IIF) assays were utilized to determine several serum biomarkers previously associated with IBD, and the decision tree algorithm was used to construct the diagnosis model. Performances of models were evaluated by prediction accuracy, precision, AUC and Matthews's correlation coefficient (MCC). The "Inflammatory Bowel Disease Multi-omics Database (IBDMDB)" cohorts were used to validate the model as external validation set. Results: The prediction rates were determined and compared for decision tree models after each data was developed using C5.0, C&RT, QUEST and CHAID. The C5.0 and CHAID algorithms, which ranked top for the prediction rate in the IBD vs. non-IBD model and the CD vs. UC model, respectively, were utilized for final pattern analysis. The final decision tree model achieved higher classification accuracy than the approach based on conservative marker combinations (sensitivity 75.0% vs. 79.5%, specificity 93.8% vs. 78.3% for differentiating IBD from non-IBD;and sensitivity 84.3% vs. 73.4%, specificity 92.5% vs. 54.9% for differentiating CD from UC, respectively). The model prediction consistency was 93% (28/30) in the external validation set. Conclusion: The decision-tree-based approach used in this study, based on serum biomarkers, has shown to be a valid and useful approach to identifying IBD and differentiating CD f

关键词： Inflamatory bowel diseases Ulcerative colitis Crohn's disease Biomarker based algorithms decision tree model

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Ontology-Based decision tree model for prediction of fatty liver diseases

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2023年第6期26卷 639-649页

作者： Banihashem, Seyed Yashar Shishehchi, Saman Buein Zahra Tech Univ Dept Elect & Comp Engn Buein Zahra Qazvin Iran

Non-Alcohol Fatty liver disease is a common clinical complication. The paper aimed to develop a knowledge-based fatty liver detection system based on an ontology and detection rules extracted from a decision tree algorithm. Ontology is created to represent knowledge related to patients and fatty liver disease. By utilizing 43 SWRL rules and the Drool inference engine in ontology, we detected fatty liver patients. The training dataset size is 70% of clean data, including 580 electronic medical records of patients who suffer from liver diseases. After inferencing the rules, the number of patients who suffer from fatty liver disease in ontology is the same as the decision tree model. The paper validated the result generated by the ontology model through the results of the decision tree model.

关键词： decision tree model disease detection fatty liver disease ontology semantic rules

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Contraceptive Use Among Korean High School Adolescents: A decision tree model

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JOURNAL OF SCHOOL NURSING 2023年第4期39卷 285-294页

作者： Kim, Sung Hae Choi, Yoona Tongmyong Univ Coll Hlth Welf & Educ Dept Nursing Busan South Korea Ulsan Coll Dept Nursing 101 Bongsu Ro Ulsan South Korea

Although the rate of sexual intercourse among adolescents has increased in Asian countries, including Korea, many sexually active adolescents still do not use contraception. The aim of this study was to identify the risk factors for contraceptive nonuse among adolescents using decision tree analysis of the 2018 Korea Youth Risk Behavior Survey data from 2,460 high school students who had an experience of sexual intercourse. The findings indicated that the highest risk group who did not use contraception during sexual intercourse did not receive sexual health education in school and was involved in habitual or purposeful drug use. The experience of ever receiving treatment due to violence and the experience of sexual intercourse after drinking were also identified as risk factors for contraceptive nonuse. To encourage contraceptive use, development of standard sexual health education, counseling, and educational intervention intended to prevent risky behaviors is needed.

关键词： adolescent contraceptive use decision tree model reproductive health

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Predicting adoption of colorectal cancer screening among Korean Americans using a decision tree model

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ETHNICITY & HEALTH 2023年第3期28卷 358-372页

作者： Jin, Seok Won Song, Christina Soyoung Univ Memphis Sch Social Work 226 McCord Hall Memphis TN 38152 USA Illinois State Univ Dept Family & Consumer Sci Fash Design & Merchandising Normal IL 61761 USA

Background Colorectal cancer screening (CRCS) rates remain suboptimal among Korean Americans despite recommendations from health organizations. Little is known about the mechanism underlying their CRCS adoption within complex systems. This study aimed to examine the multi-level predictors of CRCS adoption among Korean Americans using a decision tree model. Methods A cross-sectional survey was performed to assess CRCS adoption and multiple levels of influence - individual (i.e. CRCS self-efficacy, CRCS attitudes, risk of colorectal cancer, psychological distress, health status), interpersonal (i.e. social support, social networks, CRCS recommendations), and organizational/community (i.e. health insurance, primary doctor, primary clinic) factors. A total of 433 Korean Americans aged 50-75 in a metropolitan area in the Southeastern U.S. completed a self-report questionnaire. To determine the important variables that predict CRCS adoption, the study generated a decision tree predictive model using R statistical software. Results The results indicated that CRCS self-efficacy and CRCS attitudes at the individual level and CRCS recommendations and social support at the interpersonal level differentiate adopting or not adopting CRCS. Furthermore, CRCS recommendations (n = 138, 56%, prob = 0.64) and CRCS self-efficacy (n = 51, 21%, prob = 0.88) were the most powerful predictors of CRCS adoption. Conclusion The findings highlight the critical roles of CRCS recommendations from healthcare providers and family/friends and patients' confidence in performing screening-related tasks in influencing CRCS adoption among Korean Americans. Practice efforts should target individual and interpersonal characteristics when developing interventions for promoting CRCS among Korean Americans, especially who are not adherent to screening guidelines.

关键词： Colorectal cancer screening decision tree model social ecological model Screening adoption Korean Americans

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Development of a decision tree model for predicting the malignancy of localized gingival enlargements based on clinical characteristics

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SCIENTIFIC REPORTS 2024年第1期14卷 1-9页

作者： Sripodok, Pawat Lapthanasupkul, Puangwan Arayapisit, Tawepong Kitkumthorn, Nakarin Srimaneekarn, Natchalee Neeranadpuree, Vichaya Amornwatcharapong, Watjanee Hempornwisarn, Supawit Amornwikaikul, Sirikorn Rungraungrayabkul, Dulyapong Mahidol Univ Fac Dent Dept Oral & Maxillofacial Pathol Bangkok Thailand Mahidol Univ Fac Dent Dept Anat Bangkok Thailand Mahidol Univ Fac Dent Dept Oral Biol Bangkok Thailand Mahidol Univ Fac Dent Bangkok Thailand Mahidol Univ Fac Dent Dept Oral Med & Periodontol Bangkok Thailand

The present study aimed to determine the prevalence of localized gingival enlargements (LGEs) and their clinical characteristics in a group of Thai patients, as well as utilize this information to develop a clinical diagnostic guide for predicting malignant LGEs. All LGE cases were retrospectively reviewed during a 20-year period. Clinical diagnoses, pathological diagnoses, patient demographic data, and clinical information were analyzed. The prevalence of LGEs was determined and categorized based on their nature, and concordance rates between clinical and pathological diagnoses among the groups were evaluated. Finally, a diagnostic guide was developed using clinical information through a decision tree model. Of 14,487 biopsied cases, 946 cases (6.53%) were identified as LGEs. The majority of LGEs were reactive lesions (72.62%), while a small subset was malignant tumors (7.51%). Diagnostic concordance rates were lower in malignant LGEs (54.93%) compared to non-malignant LGEs (80.69%). Size, consistency, color, duration, and patient age were identified as pivotal factors to formulate a clinical diagnostic guide for distinguishing between malignant and non-malignant LGEs. Using a decision tree model, we propose a novel diagnostic guide to assist clinicians in enhancing the accuracy of clinical differentiation between malignant and non-malignant LGEs.

关键词： Localized gingival enlargement Gingival cancer Cancer Reactive lesion Diagnostic tree decision tree model

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