Generating concise and accurate classification rules for breast cancer diagnosis

Setiono, Rudy

doi:10.1016/s0933-3657(99)00041-x

Cited by 204 publications

(113 citation statements)

References 11 publications

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“…This algorithm can provide interpretability but, when dealing with complex data, the computation for rule extraction by recursively discretizing the activation values of the hidden unit is extremely large. Setiono in 2000 showed that by pre-processing the WBCD data, the overall accuracy can be increased to more than 98% [64].…”

Section: Annsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

212

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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

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Section: Annsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

212

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“…Rough set theory as introduced by Z. Pawlak [8] is an extension of conventional set theory that support approximations in decision making.…”

Section: Priliminaries 21 Rough Setmentioning

confidence: 99%

“…The standard intelligent method available for student database analysis data analysis are neural network [3] Bayesian classifier [4] genetic algorithms [5] decision trees [6] fuzzy set [7] . Rough set theory, Professor Z. Pawlak [8] .The theory of rough sets is a mathematical tool for deriving inference from un-certain and incomplete data base information. The rough set reduction algorithms contribute to approximate the decision classes using possibly large and simplified patterns [9].…”

Section: Introductionmentioning

confidence: 99%

Generation of Rules for Student Feedback System by the Use of Rough Set Theory

Mishra¹,

Mohanty²,

Pradhan³

et al. 2015

IJCA

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Student feedback is always a challenge for the teacher. In general method student usually in our country that is India shy in nature, usually never ask question to the teacher when he or she had any doubts in their mind , when question of feedback come they usually give negative feedback at the door of higher authorities . In this we develop rules using rough set to testify confusing state of the student mind. To generate the rules we are taking Rough Set as a tool

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“…From the large medical database which creates a need and an opportunity to ex-tract inference from data bases [1] .Data base regarding human biology have gathered large quantities of information about the patients and their physical conditions, analysis of these data provided with new medical information often resulted incomplete information [2] .Often data analysis based upon assumptions of knowledge aboutdependencies, probability theory and large number of experiments, unable to de-rive correct conclusions neither from incomplete information nor manage the data consistency. The general intelligence techniques used in medical data analysis are Neural network [3] Bayesian classifier [4] Genetic algorithms [5] Decision trees [6] Fuzzy set [7] . Rough set theory and it's basic concept was invented by Polish logician, Professor Z. Pawlak in early eighties [8] .Rough set theory is based upon conventional set theory very useful for extracting knowledge from un-certain and incomplete data based information, it assumes that we first have necessary information or knowledge of all the objects in the universe with which the objects can be divided into different groups.…”

Section: Introductionmentioning

confidence: 99%

Rough Set Approach for Generation of Classification Rules for Dengue

Mishra¹,

Mohanty²,

Hota³

et al. 2015

IJCA

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In the current age research in the field of medical science has been increased to a significant height but there are several new virus which cannot be detect by the usual medical test , for example some common disease like malaria ,dengue, hepatitis , jaundice needs of very meticulous medical analysis because all the above said dieses has very common symptoms which needs of strong analysis to determine the exact dieses. Maximum number of medical test which are conducted to determine the dieses mostly based upon doctor's guess which are not only expensive and but also give inaccurate pathological result. In this paper we emphasized more on symptom rather than pathological test .From the large domain we consider the disease hepatitis for our purpose .Every year millions of people died from hepatitis due to improper diagnosis .We develop an algorithm using rough set concept to counter hepatitis. We classified the entire paper in to three basic section 1 st section about literature review the 2 nd and 3 rd section deals with the Experiment, Findings, and Statistical Validation.

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Generating concise and accurate classification rules for breast cancer diagnosis

Cited by 204 publications

References 11 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Generation of Rules for Student Feedback System by the Use of Rough Set Theory

Rough Set Approach for Generation of Classification Rules for Dengue

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