Clustering-based approach for detecting breast cancer recurrence

Belciug, Smaranda; Salem, Abdel-Badeeh M.; Gorunescu, Florin; Gorunescu, Marina

doi:10.1109/isda.2010.5687211

Cited by 36 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Jin [9], KNN algorithm is one of the most often used classification algorithm in machine learning techniques due to its ease and resourcefulness in implementation. Belciug et al [10] presented a comparative study of cluster network, Self Organizing Map and K-means in the detection of breast cancer, using the Wisconsin Prognostic Breast Cancer (WPBC) dataset [11] in which k-means performed batter. Chaurasia and Pal [12] examined the performance of artificial neural networks (ANNs), Logistic Regression (LR), and Dyadic decision trees (DDTs) in breast cancer recurrence prediction using the Breast Cancer Dataset.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Classification Approaches for Breast Cancer

Asfaw¹

2019

IJCET

View full text Add to dashboard Cite

Breast cancer is one of the greatest common diseases among women in Africa and worldwide. Accurate and early diagnosis is very significant phase in therapy and action. However, it is not an easy one due to some doubts in detection of breast cancer. Machine learning helps us to extract information and knowledge from this the basis of past experiences and detect hard-to-perceive pattern from large and noisy dataset. This paper compares and analysis the performance of machine learning algorithms, namely Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) for detecting breast cancer. The data set used for comparison was from UCI Wisconsin original breast cancer data set. The result outcome shows that Logistic Regression performs better and classification accuracy is 96.93%.

show abstract

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Classification Approaches for Breast Cancer

Asfaw¹

2019

IJCET

View full text Add to dashboard Cite

show abstract

“…They claimed that their proposed method shows better clustering performance. Belcliug et al [6] assessed the effectiveness of three clustering algorithms using Wisconsin Recurrence Breast Cancer dataset. They compared the performance of K-means algorithm with Self-Organizing Map and a cluster network implemented on a real-time decision support system for breast cancer recurrence detection.…”

Section: Related Workmentioning

confidence: 99%

Clustering Algorithm for a Healthcare Dataset Using Silhouette Score Value

Ogbuabor¹,

Ugwoke²

2018

IJCSIT

View full text Add to dashboard Cite

show abstract

“…Mediod is very important because in the database it is that data point which is most centrally located. In order to improve the healthcare services related to public healthcare domain Lenert et al, utilize the application of k-means clustering [94] and by using the clustering technique Belciug et al detect the recurrence of breast cancer [95]. Escudero et al, used the concept of Bioprofile and K-means clustering for early detection of Alzheimer's disease [96].…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

Techniques of Data Mining In Healthcare: A Review

Ahmad¹,

Qamar²,

Rizvi³

2015

IJCA

View full text Add to dashboard Cite

Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.

show abstract

Clustering-based approach for detecting breast cancer recurrence

Cited by 36 publications

References 14 publications

Comparative Analysis of Classification Approaches for Breast Cancer

Comparative Analysis of Classification Approaches for Breast Cancer

Clustering Algorithm for a Healthcare Dataset Using Silhouette Score Value

Techniques of Data Mining In Healthcare: A Review

Contact Info

Product

Resources

About