scholarly journals Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

2021 ◽  
Vol 7 ◽  
pp. e427
Author(s):  
Nur Farahaina Idris ◽  
Mohd Arfian Ismail
Keyword(s):  
Breast Cancer ◽  
Decision Tree ◽  
The United States ◽  
Diagnostic Methods ◽  
Disease Classification ◽  
Rule Base ◽  
Cancer Disease ◽  
Inference System ◽  
Id3 Algorithm ◽  
Fuzzy Database

Breast cancer becomes the second major cause of death among women cancer patients worldwide. Based on research conducted in 2019, there are approximately 250,000 women across the United States diagnosed with invasive breast cancer each year. The prevention of breast cancer remains a challenge in the current world as the growth of breast cancer cells is a multistep process that involves multiple cell types. Early diagnosis and detection of breast cancer are among the greatest approaches to preventing cancer from spreading and increasing the survival rate. For more accurate and fast detection of breast cancer disease, automatic diagnostic methods are applied to conduct the breast cancer diagnosis. This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. This study aims to resolve the limitation of an existing method, ID3 algorithm that unable to classify the continuous-valued data and increase the classification accuracy of the decision tree. FID3 algorithm combined the fuzzy system and decision tree techniques with ID3 algorithm as the decision tree learning. FUZZYDBD method, an automatic fuzzy database definition method, would be used to design the fuzzy database for fuzzification of data in the FID3 algorithm. It was used to generate a predefined fuzzy database before the generation of the fuzzy rule base. The fuzzified dataset was applied in FID3 algorithm, which is the fuzzy version of the ID3 algorithm. The inference system of FID3 algorithm is simple with direct extraction of rules from generated tree to determine the classes for the new input instances. This study also analysed the results using three breast cancer datasets: WBCD (Original), WDBC (Diagnostic) and Coimbra. Furthermore, the comparison of FID3 algorithm with the existing methods is conducted to verify the proposed method’s capability and performance. This study identified that the combination of FID3 algorithm with FUZZYDBD method is reliable, robust and managed to perform well in breast cancer classification.

scholarly journals PERFORMANCE ANALYSIS OF BREAST CANCER CLASSIFICATION USING DECISION TREE CLASSIFIERS

2017 ◽  
Vol 9 (2) ◽  
pp. 19 ◽  
Author(s):  
P. Hamsagayathri ◽  
P. Sampath
Keyword(s):  
Breast Cancer ◽  
Decision Tree ◽  
Ductal Carcinoma ◽  
Research Work ◽  
The United States ◽  
Breast Cancer Dataset ◽  
Decision Tree Classifier ◽  
Cancer Dataset ◽  
Term Survival ◽  
Tree Classifier

Breast cancer is one of the dangerous cancers among world’s women above 35 y. The breast is made up of lobules that secrete milk and thin milk ducts to carry milk from lobules to the nipple. Breast cancer mostly occurs either in lobules or in milk ducts. The most common type of breast cancer is ductal carcinoma where it starts from ducts and spreads across the lobules and surrounding tissues. According to the medical survey, each year there are about 125.0 per 100,000 new cases of breast cancer are diagnosed and 21.5 per 100,000 women due to this disease in the United States. Also, 246,660 new cases of women with cancer are estimated for the year 2016. Early diagnosis of breast cancer is a key factor for long-term survival of cancer patients. Classification plays an important role in breast cancer detection and used by researchers to analyse and classify the medical data. In this research work, priority-based decision tree classifier algorithm has been implemented for Wisconsin Breast cancer dataset. This paper analyzes the different decision tree classifier algorithms for Wisconsin original, diagnostic and prognostic dataset using WEKA software. The performance of the classifiers are evaluated against the parameters like accuracy, Kappa statistic, Entropy, RMSE, TP Rate, FP Rate, Precision, Recall, F-Measure, ROC, Specificity, Sensitivity.

Decision Tree-Based Analytics for Reducing Air Pollution

2018 ◽  
Vol 17 (02) ◽  
pp. 1850015 ◽  
Author(s):  
Ajanta Das ◽  
Anindita Desarkar
Keyword(s):  
Air Pollution ◽  
Decision Tree ◽  
World Health ◽  
Pollution Level ◽  
Rule Base ◽  
Premature Deaths ◽  
Id3 Algorithm ◽  
Rule Sets ◽  
Term Basis ◽  
Health Organization

Air pollution indicates contaminated air which arises due to the effect of physical, biological or chemical alteration to the air in the atmosphere applicable both for indoors and outdoors. This situation arises when poisonous gases, dust or smoke enter into the atmosphere and make the surroundings vulnerable for any living beings as well as difficult for them to survive. Large numbers of premature deaths happen across the globe if exposed to these pollutants on a long-term basis as major portion of the cities have the pollution level above the threshold determined by World Health Organization (WHO). So appropriate measures need to be taken on a priority basis to reduce air pollution as well as save our planet. This paper proposes a novel air pollution reduction approach which collects source pollution data. After extraction of source data, it uses various databases (DBs) and then different decisions or classes are created. The decision tree was created with the help of Iterative Dichotomiser 3 (ID3) algorithm to implement the rule base appropriately depending on the air pollution level and a bunch of rule sets were derived from the decision tree further.

scholarly journals A new paradigm of screening and early diagnosis: assessment of benefits and harms

2020 ◽  
Vol 66 (6) ◽  
pp. 589-602
Author(s):  
Давид Заридзе ◽  
Dmitry Maksimovich ◽  
Ivan Stilidi
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Prostate Cancer ◽  
Clinical Trials ◽  
Early Diagnosis ◽  
The United States ◽  
Diagnostic Methods ◽  
New Paradigm

Abstract The article presents scientific evidence that confirms the new paradigm that  “early” diagnosis is not always beneficial, and that screening and early diagnosis can do more harm than good. As a result, of screening, in a number of cases, lesions are diagnosed that, although have histological patterns of cancer, are often clinically insignificant, indolent i.e. overdiagnosis takes place. Such lesions primarily include latent cancers of the prostate and thyroid gland. An increase in the incidence of certain types of cancers in the United States and other developed countries, as a result, of the introduction of PSA screening, mammography, ultrasound examination of the neck and other highly sensitive diagnostic methods, with stable or decreasing mortality, is a sign of overdiagnosis. In Russia, there is also a marked increase in the incidence of cancer of the prostate, breast, thyroid, kidney and melanoma, while mortality from these forms of cancer is stable or decreasing. The increase in the incidence of all malignant formations in Russian, as in American men, is determined by the increase in the incidence of prostate cancer. In randomized clinical trials of the efficacy of screening for prostate and breast cancer, an excess of the detected cases of cancer in the screening group compared with the control group indicates overdiagnosis. With an increase in follow-up (10-15 years), the number of excess cases in the screening group decreases. However, in some studies even after 10-15 years of follow-up, the excess of cancer cases in the screening group persisted, i.e. overdiagnosis was confirmed. Thus, the problem of overdiagnosis is also relevant to controlled clinical trials, despite a well-verified protocol and strict adherence to it. The danger of overdiagnosis in real life, daily practice, and especially with opportunistic screening, which, by definition, is carried out without quality control, is much higher. Overdiagnosis often leads to unnecessary, sometimes excessive treatment and a deterioration in the quality of life of patients who are not cancer patients. Refusal of aggressive therapy and active follow-up should be the method of choice for the management of patients with asymptomatic neoplasms identified at the screening. Such tactics will avoid unnecessary and excessive interventions, which, in turn, will prevent a deterioration in the quality of life of patients and, in addition, will reduce the cost of treatment. Key words: overdiagnosis, screening, early diagnosis, trends in incidence and mortality, prostate cancer, breast cancer, thyroid cancer

Association Between Breast Cancer Disease Progression and Workplace Productivity in the United States

2017 ◽  
Vol 59 (2) ◽  
pp. 198-204 ◽  
Author(s):  
Wesley Yin ◽  
Ruslan Horblyuk ◽  
Julia Jane Perkins ◽  
Steve Sison ◽  
Greg Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
United States ◽  
Disease Progression ◽  
The United States ◽  
Cancer Disease ◽  
Workplace Productivity

scholarly journals Fuzzy Decision Tree to Predict Student Success in Their Studies

2020 ◽  
Vol 1 (3) ◽  
pp. 135-144
Author(s):  
Heri Bambang Santoso
Keyword(s):  
Decision Tree ◽  
Fuzzy Inference ◽  
Drop Out ◽  
Classification Rules ◽  
University Management ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Inference System ◽  
Target Time ◽  
Id3 Algorithm

The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.

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