Different Machine Learning Algorithms for Breast Cancer Diagnosis

Aloraini, Adel

doi:10.5121/ijaia.2012.3603

Cited by 28 publications

(12 citation statements)

References 1 publication

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“…The motivation of our research is based on a thorough literature search on the application of different ML models in cancer research. Large parts of these models have good performance on cancer data [18,19], which emphasizes the experience of using ML techniques in cancer, in particular in breast cancer research. The use of ML also gives some more freedom in model design, targeting the improvement in accuracy and lower error rate in cancer diagnosis [8].…”

Section: Related Workmentioning

confidence: 86%

Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies

2019

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The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective of the presented study. The paper discusses an approach to the problem in which the main factor used to predict survival time is the originally developed tumor-integrated clinical feature, which combines tumor stage, tumor size, and age at diagnosis. Two datasets from corresponding breast cancer studies are united by applying a data integration approach based on horizontal and vertical integration by using proper document-oriented and graph databases which show good performance and no data losses. Aside from data normalization and classification, the applied machine learning methods provide promising results in terms of accuracy of survival time prediction. The analysis of our experiments shows an advantage of the linear Support Vector Regression, Lasso regression, Kernel Ridge regression, K-neighborhood regression, and Decision Tree regression—these models achieve most accurate survival prognosis results. The cross-validation for accuracy demonstrates best performance of the same models on the studied breast cancer data. As a support for the proposed approach, a Python-based workflow has been developed and the plans for its further improvement are finally discussed in the paper.

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Section: Related Workmentioning

confidence: 86%

Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies

2019

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“…The mean did slightly better than the best of the five methods reported in [24] fresh approach should now be explored further on a wider set of benchmarks.…”

Section: Segments 7 Results and Comparisionsmentioning

confidence: 99%

Heuristic Methods to Identify Fuzzy Measures Applied to Choquet Integral Classification of Breast Cancer Data

Adams¹

2015

IJDIWC

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When the search space for optimisation or classification problems gets large heuristics and soft computing are often used to find the needed parameters. The Choquet integral methods needs one parameter for each subset of the condition attributes because these sets form a lattice on which the fuzzy measure needed for integration is defined. In this paper the frequency of how many data from each class pass through each set in the lattice provides information that is then used to construct the fuzzy measure. Several heuristics based on this idea are tested upon the well-known Wisconsin breast cancer data set. The heuristics prove to be as successful, or more so, than a range of other methods quoted in the literature when 10-fold cross validation is performed.In order for the Choquet to separate classes after integration a boundary value has to be calculated.Remarkably, it is found that switching the way to view success from number of records correctly classified to the distance of the wrongly classified record from the boundary leads to a hill climbing approach that can reclassify the data with a 100% success rate.

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“…Each node represents some information. Decision tree learning is started from the root node and discrete values are produced at each node by testing the values of attribute [1]. These discrete values acts as target function.…”

Section: Decision Trees (Dt)mentioning

confidence: 99%

Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms

Oo¹

2018

IJASRE

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Data mining (DM) can be viewed as a result of the natural evolution of information technology. The role of data mining approach is very important in computer science and knowledge engineering. A number of data mining approaches are used for classification. Classification is the process of finding a model that describes and distinguishes data classes or concepts. The decision tree (DT) approach is most useful in the classification problem. The research work analyses the efficiency of the Principal Component Analysis (PCA) based decision tree algorithms, namely J48, Classification and Regression Tree (CART) and Random Forest.

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Different Machine Learning Algorithms for Breast Cancer Diagnosis

Abstract: ABSTRACT

Cited by 28 publications

References 1 publication

Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies

Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies

Heuristic Methods to Identify Fuzzy Measures Applied to Choquet Integral Classification of Breast Cancer Data

Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms

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