An optimized K-Nearest Neighbor based breast cancer detection

Assegie, Tsehay Admassu

doi:10.18196/jrc.2363

Cited by 63 publications

(44 citation statements)

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“…Chi-square compares two input features and examines if they are related. Mathematically, chi-square is defined as (1).…”

Section: Methodsmentioning

confidence: 99%

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Exploring the performance of feature selection method using breast cancer dataset

Assegie¹,

Tulasi²,

Kumar

2022

IJEECS

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Breast cancer is the most common type of cancer occurring mostly in females. In recent years, many researchers have devoted to automate diagnosis of breast cancer by developing different machine learning model. However, the quality and quantity of feature in breast cancer diagnostic dataset have significant effect on the accuracy and efficiency of predictive model. Feature selection is effective method for reducing the dimensionality and improving the accuracy of predictive model. The use of feature selection is to determine feature required for training model and to remove irrelevant and duplicate feature. Duplicate feature is a feature that is highly correlated to another feature. The objective of this study is to conduct experimental research on three different feature selection methods for breast cancer prediction. Sequential, embedded and chi-square feature selection are implemented using breast cancer diagnostic dataset. The study compares the performance of sequential embedded and chi-square feature selection on test set. The experimental result evidently shows that sequential feature selection outperforms as compared to chi-square (X<sup>2</sup>) statistics and embedded feature selection. Overall, sequential feature selection achieves better accuracy of 98.3% as compared to chi-square (X<sup>2</sup>) statistics and embedded feature selection.

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“…Chi-square compares two input features and examines if they are related. Mathematically, chi-square is defined as (1).…”

Section: Methodsmentioning

confidence: 99%

“…Breast cancer is the most common cause of death among women throughout the global population [1], [2]. Breast cancer causes the second prevalent number of deaths in women [3].…”

Section: Introductionmentioning

confidence: 99%

Exploring the performance of feature selection method using breast cancer dataset

Assegie¹,

Tulasi²,

Kumar

2022

IJEECS

Self Cite

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“…Mahin et al set the K value through Geometric Mean (G-mean), and applied it to the unbalanced datasets [23]. Assegie selected the optimal K value based on the misclassification rate, and applied the proposed KNN algorithm to breast cancer detection [24].…”

Section: K Value Calculationmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang¹,

Li²

2021

IEEE Trans. Knowl. Data Eng.

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KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.

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“…Researchers have also conducted another study on the problem of kidney disease identification by applying adaptive boosting algorithm [13]. The researchers developed a model using adaptive boosting and decision tree algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the challenge in identification of chronic kidney disease, the disease has become one of the world's deadliest diseases. The report shows that there are roughly 2.5 to 11.25 million cases chronic kidney disease worldwide [2][3][4][5][6][7][8][9][10][11][12][13]. Different machine learning algorithms such as support vector machine (SVM) [2] and boosting classifiers [4] are applied to kidney disease data repository to create predictive model with acceptable level of accuracy to identify chronic kidney disease as early as possible.…”

Section: Introductionmentioning

confidence: 99%

Automated Chronic Kidney Disease Detection Model with Knearest Neighbor

Assegie

2021

IJCIT

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Chronic kidney disease is one of the most common disease in the world today. Kidney disease causes death if the patient is not threated at early stage. One of the challenge in kidney disease treatment is accurate identification of kidney disease at an early stage. Moreover, detecting kidney disease requires experienced nephrologist. However, in developing nations lack of medical specialist or nephrologist for identifying chronic kidney disease makes the problem more challenging. As alternative solution to kidney disease identification, researchers have developed many intelligent models using K-nearest Neighbors (KNN) algorithm. However, the accuracy of the existing KNN model has scope for improvement. Thus, this study proposed KNN based model for accurate identification of kidney disease at early stage. To develop optimized KNN model, we have employed error plot to find most favorable K value to obtain more accurate result than the existing models. To conduct experiments, study employed kidney disease dataset collected form publically available Kaggle data repository for training and testing the proposed model. Finally, we have evaluated the proposed model against predictive accuracy. The experimental result on the proposed model appears to prove that the predictive accuracy of the model is 99.86%.

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An optimized K-Nearest Neighbor based breast cancer detection

Cited by 63 publications

References 0 publications

Exploring the performance of feature selection method using breast cancer dataset

Exploring the performance of feature selection method using breast cancer dataset

KNN Classification with One-step Computation

Automated Chronic Kidney Disease Detection Model with Knearest Neighbor

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