A topological reduction for predicting of a lung cancer disease based on generalized rough sets

2021 ◽  
pp. 1-16
Author(s):  
M. K. El-Bably ◽  
E. A. Abo-Tabl
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Decision Making ◽  
Binary Relation ◽  
Rough Sets ◽  
Medical Application ◽  
Cancer Disease ◽  
Hypothetical Data ◽  
Generalized Rough Sets ◽  
Cancer Diseases

The present work proposes new styles of rough sets by using different neighborhoods which are made from a general binary relation. The proposed approximations represent a generalization to Pawlak’s rough sets and some of its generalizations, where the accuracy of these approximations is enhanced significantly. Comparisons are obtained between the methods proposed and the previous ones. Moreover, we extend the notion of “nano-topology”, which have introduced by Thivagar and Richard [49], to any binary relation. Besides, to demonstrate the importance of the suggested approaches for deciding on an effective tool for diagnosing lung cancer diseases, we include a medical application of lung cancer disease to identify the most risk factors for this disease and help the doctor in decision-making. Finally, two algorithms are given for decision-making problems. These algorithms are tested on hypothetical data for comparison with already existing methods.

Different kinds of generalized rough sets based on neighborhoods with a medical application

2021 ◽  
pp. 2150086
Author(s):  
Mostafa K. El-Bably ◽  
Tareq M. Al-shami
Keyword(s):  
Lung Cancer ◽  
Rough Set ◽  
Rough Sets ◽  
Critical Role ◽  
Medical Application ◽  
Approximation Space ◽  
Cancer Disease ◽  
Hypothetical Data ◽  
The Universe ◽  
Generalized Rough Sets

Approximation space can be said to play a critical role in the accuracy of the set’s approximations. The idea of “approximation space” was introduced by Pawlak in 1982 as a core to describe information or knowledge induced from the relationships between objects of the universe. The main objective of this paper is to create new types of rough set models through the use of different neighborhoods generated by a binary relation. New approximations are proposed representing an extension of Pawlak’s rough sets and some of their generalizations, where the precision of these approximations is substantially improved. To elucidate the effectiveness of our approaches, we provide some comparisons between the proposed methods and the previous ones. Finally, we give a medical application of lung cancer disease as well as provide an algorithm which is tested on the basis of hypothetical data in order to compare it with current methods.

scholarly journals Two Different Views for Generalized Rough Sets with Applications

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2275
Author(s):  
Radwan Abu-Gdairi ◽  
Mostafa A. El-Gayar ◽  
Mostafa K. El-Bably ◽  
Kamel K. Fleifel
Keyword(s):  
Decision Making ◽  
Information System ◽  
Rough Set ◽  
Rough Sets ◽  
Real Life ◽  
Binary Relations ◽  
Medical Field ◽  
Good Decision ◽  
Heart Attacks ◽  
Generalized Rough Sets

Rough set philosophy is a significant methodology in the knowledge discovery of databases. In the present paper, we suggest new sorts of rough set approximations using a multi-knowledge base; that is, a family of the finite number of general binary relations via different methods. The proposed methods depend basically on a new neighborhood (called basic-neighborhood). Generalized rough approximations (so-called, basic-approximations) represent a generalization to Pawlak’s rough sets and some of their extensions as confirming in the present paper. We prove that the accuracy of the suggested approximations is the best. Many comparisons between these approaches and the previous methods are introduced. The main goal of the suggested techniques was to study the multi-information systems in order to extend the application field of rough set models. Thus, two important real-life applications are discussed to illustrate the importance of these methods. We applied the introduced approximations in a set-valued ordered information system in order to be accurate tools for decision-making. To illustrate our methods, we applied them to find the key foods that are healthy in nutrition modeling, as well as in the medical field to make a good decision regarding the heart attacks problem.

Early Mortality in Patients with Lung Cancer: Risk Factors for Death within 30 Days or 60 Days of Initial Diagnosis

2019 ◽  
Author(s):  
A Tufman ◽  
S Schneiderbauer ◽  
D Kauffmann-Guerrero ◽  
F Manapov ◽  
C Schneider ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Cancer Risk ◽  
Lung Cancer Risk ◽  
Early Mortality ◽  
Initial Diagnosis ◽  
Cancer Risk Factors

scholarly journals The homogeneous and heterogeneous risk factors for occurrence and prognosis in lung cancer patients with bone metastasis

2019 ◽  
Vol 17 ◽  
pp. 100251 ◽  
Author(s):  
Ben Wang ◽  
Lijie Chen ◽  
Chongan Huang ◽  
Jialiang Lin ◽  
Xiangxiang Pan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Lung Cancer Patients

The prevalence and risk factors associated with preoperative deep venous thrombosis in lung cancer surgery

Surgery Today ◽  
2021 ◽  
Author(s):  
Toshiki Takemoto ◽  
Junichi Soh ◽  
Shuta Ohara ◽  
Toshio Fujino ◽  
Takamasa Koga ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Venous Thrombosis ◽  
Deep Venous Thrombosis ◽  
Lung Cancer Surgery ◽  
Cancer Surgery ◽  
Factors Associated

scholarly journals Development of a prognostic tool: based on risk factors for tooth loss after active periodontal therapy

2021 ◽  
Author(s):  
Sonja Rahim-Wöstefeld ◽  
Dorothea Kronsteiner ◽  
Shirin ElSayed ◽  
Nihad ElSayed ◽  
Peter Eickholz ◽  
...  
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Tooth Loss ◽  
Treatment Plan ◽  
Periodontal Therapy ◽  
Outcome Variable ◽  
Individual Treatment ◽  
Risk Indicator ◽  
Related Factors ◽  
Prognostic Tool

Abstract Objectives The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT). Material and methods Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting. Results Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77. Conclusion This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value. Clinical relevance In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.

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