Application of kNN and SVM to predict the prognosis of advanced schistosomiasis

Zhou, Xiaorong; Wang, He; Xu, Chuan; Li, Peng; Xu, Feng; Lian, Lifei; Deng, Gang; Ji, Suqiong; Hu, Mengyan; Zhu, Hong; Xu, Yi; Li, Guo

doi:10.1007/s00436-022-07583-8

Cited by 16 publications

(6 citation statements)

References 13 publications

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“…SVM is of great value in clinical diagnosis, and the probability value of the disease can be calculated according to the relevant information of the patient. Zhou et al used two machine learning algorithms, k-nearest neighbor (KNN) and support vector machine (SVM), to construct a prognostic prediction model for small samples of patients with advanced schistosomiasis, and recommended SVM as the best model by comparing AUC [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

Lei

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

Lei

et al. 2023

Heliyon

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“…In comparison to the RF algorithm, XGBoost employs an adaptive gradient boosting algorithm that can automatically select the optimal splitting point and tree depth, thus improving prediction performance. Furthermore, XGBoost takes into account regularization and effectively avoids model overfitting ( Zhou et al., 2022 ). Although the KNN algorithm has higher accuracy and can avoid overfitting problems, it has high computational complexity when searching for K nearest neighbors in the training set for each test sample and calculating their distances for classification or regression prediction.…”

Section: Discussionmentioning

confidence: 99%

Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis

Liu

Wang

et al. 2023

Front. Cell. Infect. Microbiol.

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BackgroundCombining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment.MethodsWithin the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models.ResultsIn comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and H. pylori infection.ConclusionThe XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.

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“…194 The performance of the KNN classifier is heavily influenced by the selection of the distance metric, as evidenced by notable performance variations among different distance metrics. 195 Notably, the KNN classifier's efficiency in healthcare applications was validated by Zhou et al, 196 who found it beneficial for health planning and management, especially in addressing late-stage schistosomiasis. Unlike some models, KNN requires no training.…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Xu,

Zheng,

et al. 2023

J Evidence Based Medicine

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BackgroundIncreasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data‐rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.MethodsWe conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.ResultsOur study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision‐making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.ConclusionAdvanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

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Application of kNN and SVM to predict the prognosis of advanced schistosomiasis

Cited by 16 publications

References 13 publications

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

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