Analysis of survival for lung cancer resections cases with fuzzy and soft set theory in surgical decision making

Alcantud, José Carlos R.; Varela, Gonzálo; Santos-Buitrago, Beatriz; Santos‐García, Gustavo; Jiménez, Marcelo F.

doi:10.1371/journal.pone.0218283

Cited by 36 publications

(14 citation statements)

References 69 publications

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“…Although our study was a retrospective cohort study, we used PS-adjusted multivariate analysis to eliminate confounding factors as much as possible. As another approach, artificial intelligence such as a fuzzy and soft set theory or an ensemble model of an artificial neural network, which were used to predict postoperative mortality or morbidity after lung resection 28,29 , may be available to predict prognostic factors after the start of BSC alone. Second, because there has been remarkable progress in the treatment of NSCLC, such as the introduction of molecular targeted agents or immune checkpoint inhibitor antibodies 30 , the toxicities of these anticancer treatments are relatively reduced as compared to those of other kinds of treatment.…”

Section: Discussionmentioning

confidence: 99%

Decision-making factors for best supportive care alone and prognostic factors after best supportive care in non-small cell lung cancer patients

Kitazawa

Takeda

Naka

et al. 2019

Sci Rep

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Among patients with non-small cell lung cancer (NSCLC), best supportive care (BSC) is well-known to improve patient’s quality of life and prolong survival. This study aimed to clarify (1) the decision-making factors of BSC alone and (2) the prognostic factors after selection of no further anticancer therapies. We retrospectively reviewed the clinical data of patients with NSCLC between November 2004 and February 2014, who received BSC as only therapy and BSC after completion of anticancer therapies. One hundred eighteen patients received BSC alone. Among 860 patients treated with anticancer therapies, 236 were selected as control group, 160 of whom received BSC after anticancer therapy. The significant reasons for receiving BSC alone were: comorbidities of dementia, poor Eastern Cooperative Oncology Group performance status (ECOG-PS), patients’ wishes, pulmonary comorbidities, wild type epidermal growth factor receptor (EGFR), relevant social background and psychiatric comorbidities. Poor prognostic factors at the start of BSC were poor ECOG-PS, presence of disseminated intravascular coagulation (DIC), and history of anticancer therapy. NSCLC patients with comorbidities, wild type EGFR, and relevant social background factors tended to receive BSC alone. Post-cancer therapy NSCLC patients and those with DIC and declining ECOG-PS have a shorter survival period from the start of BSC.

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Section: Discussionmentioning

confidence: 99%

Decision-making factors for best supportive care alone and prognostic factors after best supportive care in non-small cell lung cancer patients

Kitazawa

Takeda

Naka

et al. 2019

Sci Rep

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“…Having a tool to accurately predict the survival time of oral cancer patients could help regulate the effects of psychological distress on physical and mental health outcomes after diagnosis. Medical decision-making tools based on fuzzy and soft set theories and artificial intelligence are effective for determination of cancer survival and enhancing disease awareness[ 16 ]. Awareness of the disease can lessen the burden of the disease on the survivors and their caretakers, and assist with medical and dental decision-making moving forward.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival

Hung

Park

Hon

et al. 2020

WJCO

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BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide. Public knowledge in oral cancer risk factors and survival is limited. AIM To come up with machine learning (ML) algorithms to predict the length of survival for individuals diagnosed with oral cancer, and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival. METHODS We used the Surveillance, Epidemiology, and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables. Four ML techniques in the area of artificial intelligence were applied for model training and validation. Model accuracy was evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R 2 and adjusted R 2 . RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis, primary cancer site, tumor size and year of diagnosis. Year of diagnosis referred to the year when the tumor was first diagnosed, implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past. The extreme gradient boosting ML algorithms showed the best performance, with the MAE equaled to 13.55, MSE 486.55 and RMSE 22.06. CONCLUSION Using artificial intelligence, we developed a tool that can be used for oral cancer survival prediction and for medical-decision making. The finding relating to the year of diagnosis represented an important new discovery in the literature. The results of this study have implications for cancer prevention and education for the public.

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“…The output result of various classifiers used in Weka tool on lung cancer data is represented in below table. Generally in confusion matrix Accuracy, Recall, Precision and F-Measure are the key process parameter for classification [ 4 , 14 ]. Classification accuracy is the measure of number of correct prediction made out from total number of prediction.…”

Section: Results Analysismentioning

confidence: 99%

Prediction of Lung Cancer Using Machine Learning Classifier

Patra

2020

Communications in Computer and Information Science

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Lung cancer generally occurs in both male and female due to uncontrollable growth of cells in the lungs. This causes a serious breathing problem in both inhale and exhale part of chest. Cigarette smoking and passive smoking are the principal contributor for the cause of lung cancer as per world health organization. The mortality rate due to lung cancer is increasing day by day in youths as well as in old persons as compared to other cancers. Even though the availability of high tech Medical facility for careful diagnosis and effective medical treatment, the mortality rate is not yet controlled up to a good extent. Therefore it is highly necessary to take early precautions at the initial stage such that it’s symptoms and effect can be found at early stage for better diagnosis. Machine learning now days has a great influence to health care sector because of its high computational capability for early prediction of the diseases with accurate data analysis. In our paper we have analyzed various machine learning classifiers techniques to classify available lung cancer data in UCI machine learning repository in to benign and malignant. The input data is prepossessed and converted in to binary form followed by use of some well known classifier technique in Weka tool to classify the data set in to cancerous and non cancerous. The comparison technique reveals that the proposed RBF classifier has resulted with a great accuracy of 81.25% and considered as the effective classifier technique for Lung cancer data prediction.

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Analysis of survival for lung cancer resections cases with fuzzy and soft set theory in surgical decision making

Cited by 36 publications

References 69 publications

Decision-making factors for best supportive care alone and prognostic factors after best supportive care in non-small cell lung cancer patients

Decision-making factors for best supportive care alone and prognostic factors after best supportive care in non-small cell lung cancer patients

Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival

Prediction of Lung Cancer Using Machine Learning Classifier

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