Improved Framework for Breast Cancer Prediction Using Frequent Itemsets Mining for Attributes Filtering

Sinha, Ankita; Sahoo, Bandya; Rautaray, Siddharth Swarup; Pandey, Manjusha

doi:10.1109/iccs45141.2019.9065877

Cited by 7 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Frequent item-set mining is used to select the essential features in patients' datasets. The decision tree, Naive Bayes (NB), k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM) are compared, and it is found that SVM outperforms other models ( 23 ). A research paper focused on reducing erroneous prediction results that is false positive and false negative.…”

Section: Literature Reviewmentioning

confidence: 99%

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid

Batool

Kim

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid

Batool

Kim

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Sinha et al [47] introduced attribute filtering strategies, such as frequent itemsets mining, to identify the most important and applicable attribute from the Wisconsin BC dataset using a classification algorithmic such as SVM. Attribute filtering was used to compare NB, K-NN, and DT.…”

Section: Related Workmentioning

confidence: 99%

“…Comparing with the previous works, the provided method acquires a high accuracy classification of breast cancer. However, researchers in[47] and[45] used WBC (original) dataset to train and test different DM algorithms. They respectively registered anaccuracy of 96.61% (SVM) and 98.13% (CART), despite a high execution time of CART.…”

mentioning

confidence: 99%

A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Khorshid

Abdulazeez

Sallow

2021

AJRCoS

View full text Add to dashboard Cite

Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

show abstract