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

Idris, Nur Farahaina; Ismail, Mohd Arfian

doi:10.7717/peerj-cs.427

Cited by 31 publications

(8 citation statements)

References 68 publications

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“…In this study, we adopted several common and widely applied machine learning methods, including KNN [24], DT [25,26],…”

Section: Machine Learning Methods and Baselinesmentioning

confidence: 99%

Application and comparison of several machine learning methods in the prognosis of cervical cancer

2022

European Journal of Gynaecological Oncology

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Accurate prognosis of cervical cancer in the clinical setting is challenging because of the complexity of the causative factors. Considering the drawbacks of the widely used Cox proportional hazards model, such as the inability to fully use the information and the possible failure to achieve the best fit, several new attempts based on machine learning have been developed to find better prognostic prediction models. However, the application of these attempts is often limited, because they often rely on public databases. Therefore, for cervical cancer, there is a need to explore the value of machine learning in terms of its practical application in prognostic prediction. In this study, we introduced several machine learning methods including k-nearest neighbors (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) to predict the survival of patients by using the real-world pathological data of 216 patients collected from the Fifth People's Hospital of Chengdu. The experimental results showed that these methods have a promising application value in the prediction of overall survival (OS) of patients with cervical cancer

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“…In this study, we adopted several common and widely applied machine learning methods, including KNN [24], DT [25,26],…”

Section: Machine Learning Methods and Baselinesmentioning

confidence: 99%

Application and comparison of several machine learning methods in the prognosis of cervical cancer

2022

European Journal of Gynaecological Oncology

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“…Classification is a data mining technique that involves a knowledge extraction process [13]. Classification can be referred to as categorizing the indeterminate data into discrete classes or categories (target variables) based on specific features and variables [14][15][16]. Meanwhile, it is for regression tasks involving continuous features on the target variable.…”

Section: Classificationmentioning

confidence: 99%

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Idris,

Ismail,

Jaya

et al. 2024

PLoS ONE

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Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

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“…The ID3 algorithm utilizes the knowledge of information entropy to select important features, and its underlying theory is relatively simpler and clearer [28][29].…”

Section: Advantages and Disadvantages Of The Id3 Algorithmmentioning

confidence: 99%

Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm

2023

Applied Mathematics and Nonlinear Sciences

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This paper utilizes the data mining decision tree ID3 algorithm to improve the traditional ID3 algorithm, exclude the influence of other factors, and realize the decision tree ID3 algorithm by using the data mining decision tree ID3 algorithm in the database of college students’ mental health assessment in the application of mental health assessment in colleges and universities as an example. Among them, the number of nodes, the number of rules, the classification accuracy and the time of constructing the decision tree of the algorithm are compared to verify the improvement effect of the ID3 algorithm. The target dataset consists of psychological assessment data of students, which includes their basic situation and nine-dimensional psychological symptoms. Analyze the recorded data of students’ mental health status, extract the information on personality, parental relationship, economic income, and psychological abnormality, set the decision tree analysis variables, assign the value of students’ health status, and derive the specific factors affecting students’ mental health by using the decision tree If-Then classification rules. The validation results show that the generated decision tree ID3 model cross-validation estimate is 0.261, the standard error is 0.016, and its obtained standard error is less than 0.018, which indicates that the model fits better.

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

Cited by 31 publications

References 68 publications

Application and comparison of several machine learning methods in the prognosis of cervical cancer

Application and comparison of several machine learning methods in the prognosis of cervical cancer

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm

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