Optimization of RFM for automated breast cancer detection

PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018)

One of cancer that commonly causes most of the death is breast cancer. According to WHO data published in 2017, breast cancer deaths in Indonesia reached 21,287 or 1.27 % of total deaths. Delay in knowing of the condition of breast cancer in women with breast cancer, results in increased mortality, poor prognosis, and decreased survival rates, which are also associated with lower awareness of breast cancer, and also recommended non-adherence to screening. In this paper, we propose a random forest for breast cancer prediction. Random forest is one of many classification techniques, and it is an algorithm for big data classification. Random forest classification is applied to cancer microarray data to achieve a more accurate and reliable classification performance. The accuracy in this paper is 100 %.

IRJET, 2020

Breast Cancer is the most leading malignancy affecting 2.1 million women each year which leads to greatest number of deaths among women. Early treatment not only helps to cure cancer but also helps in prevention of its recurrence. And hence this system mainly focuses on prediction of breast cancer where it uses different machine learning algorithms for creating models like decision tree, logistic regression, random forest which are applied on pre-processed data which suspects greater accuracy for prediction. Amongst all the models, Random Forest Classification leads to best accuracy with 98.6%. These techniques are coded in python and uses numpy, pandas, seaborn libraries.

Biomedical Journal of Scientific & Technical Research

The early detection and diagnosis of diseases, which are made possible by artificial intelligence, enable us to begin treatment protocols timely in the early stages of disease progression, which increases the likelihood of treatment and recovery. Artificial intelligence plays an important role in the improvement of healthcare for patients. One out of every eight women will get breast cancer in their lifetime. Worldwide, regionally, and locally, breast cancer is the most frequent kind of cancer (plus 1 in 870 men). Follow up on the recovery and treatment of the patient and predicting whether at the level of the individual or at the level of his family or the generation emerging from it in the future at the long term to prevent it and prognosticate it This paper proposes to present a non-invasive method for diagnosing and classifying breast diseases using mammograms, to identify breast tissue as normal, or abnormal (malignant tumor, or a benign tumor), and to identify breast tissue as normal, or abnormal (malignant tumor, or a Supervised Learning algorithms, primarily Random Forest (RF) and Decision Tree (DT) Classifiers, are used to classify breast tissue as either malignant or benign based on a number of features that can reach up to thirty features, including the breast mass, its size, diameter, and circumference, and whether or not the tumor is a cyst or a solid tumor. These features are primarily the most important factors in the classification process. The results showed that RF achieved higher accuracy, which is reached up to 96.9 percent, followed by DT classifier with 95 percent respectively, and finally the model is evaluated using 10 folds cross-validation (CV), which is the best method to evaluate the model, because it depends resampling that uses different folds of the data to test and train a model on different iterations to make the model more generalization.

Biomedical Signal Processing and Control, 2021

Decision tree algorithm is one of the algorithm which is easily understandable and interpretable algorithm used in both training and application purpose during breast cancer prognosis. To address this problem, Random Decision Forests are proposed. In this manuscript, the breast cancer classification can be determined by combining the advantages of Feature Weight and Hyper Parameter Tuned Random Decision Forest classifier. Here the Kernel Neutrosophic C-Means Clustering is used as Feature Weight, which allocates greater weights to applicable features and smaller weights to less applicable features. Then Random Decision Forest classifier model are optimized with the help of the Bayesian Optimization algorithm to obtain optimal hyper tuning parameters. By this, the accurate classification of breast cancer is successfully achieved. Then the efficiency of the proposed system is executed in python. The performance analysis are executed in Wisconsin prognostic Breast Cancer (WPBC) dataset, 70 % training and remaining 30 % testing is compared with the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of proposed feature weight and Random Decision Forest Classifier with Bayesian Optimization (FW + BOA-RDF) in Breast Cancer Wisconsin (Prognosis) Data Set is 6.66 %, 12.659 % and 37.618 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively. The performance analysis in Wisconsin prognostic Breast Cancer (WPBC) dataset, 75 % training and the remaining 25 % testing is compared at Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of FW + BOA-RDF in Breast Cancer Wisconsin (Prognosis) Data Set is 3.7146 %, 5.27398 % and 4.4413 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively.

International Journal of Experimental Research and Review, 2024

Cancer in breasts appears as a terrible malediction in society. It snitches huge human lives across the world and its peril is going to increase at a startling rate. Identification of this disease at the initial stages is indispensable. In many cases, traditional methods are prone to errors and protracted. Models applying machine learning approaches have been shown fruitful in this application area. There are large numbers of approaches in machine learning which demonstrate impressive results. This research strives to take out the short comings from the existing models and, by resolving the underlying technical issues, deliver higher accuracy in end results. The research motivates and endeavours to make the patients' treatment processes more justified and cost-effective. The research works with WDBC dataset for breast cancer, which is publicly accessible from the UCI research database. This study uses multiple individual learners, namely Support Vector Machines (SVM), Logistic Regression(LR), Random Forest(RF), Naive Bayes(NB), K-Nearest Neighbours(K-NN), Decision Tree(DT) and an ensemble learner called Gradient Boosting(GB) with multiple techniques of feature selection namely Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). The experimental techniques discern subtle patterns within the dataset. The proposed model evaluates the results and performances through metrics specificity, sensitivity and accuracy in a comparative structure. It succeeds with higher accuracy of 98%. The study highlights its potential as a significant tool in medical diagnostics.

IOP Conference Series: Materials Science and Engineering, 2021

Breast cancer disease is recognized as the common extensive malignant tumor in between women. Identification of the initial stage of malignant growth may treatment of this disease. Early treatment helps to alleviate the disease and helps anticipate its recurrence in women. Experts have used some fact checks and different medical methods or equipment to improve the accuracy of conclusions in clinical medical service management. In this article, it extensively discussed the implementation of data mining strategies to detection as well as prediction of breast malignant tumors, including random forest (RF), support vector classifier (SVC), k-nearest neighbors (KNN), linear discriminant analysis (LDA), Gradient Boosting Classifier (GBC), Decision Tree (DT) In addition, principal component analysis (PCA) to underline changes and show strong patterns in the informational index. The connection framework is likewise used to show the level of close relationship between attributes. The sequential feature selection (SFS) method is used for comparing the accuracy of a data set with all features and the accuracy of a classifier with selected features. The results show that RF_sfs, KNN_sfs, SVC_rbf and SVC_sfs have the highest and equal accuracy, which is 97.66%. They perform well and can predict the growth of harmful malignant tumors.

Eswar Publications, 2024

Breast cancer is the main reason for mortality in women. It was very difficult to predict breast cancer in the early stages by doctors and pathologists. They need some automated tools to make an early prediction of cancer and diagnosis as soon as possible. Some research found that Machine learning (ML) algorithm helps them to take decisions and perform diagnosis based on data collected by the medical field. In this paper, we use various ML algorithms and classifiers like K-NearestNeighbors (KNN), Support Vector Machine (SVM) and Random Forest (RF) to find the accurate result of cancer in less intervals of time. It has been found that Support Vector Machine has highest accuracy 98.83% and Random Forest has second highest accuracy with 98.24% among all other models.

Breast cancer is considered one of the biggest killers in women globally. The major reason of mortality is the reason that cancer is diagnosed at later stages. Objectively, this study is conducted to compare evaluation metrics of 6 ML models such as Naïve Bayes, k-Nearest Neighborhood (K-NN's), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) on Wisconsin Breast Cancer (BC) Dataset. WEKA tool has been used to calculate the performance evaluation of these supervised ML algorithms. The literature shows that the Weka tool has been widely used in various data mining problems. The results clearly show that two models have achieved better accuracy, recall and other performance metrics in order to identify risk of breast cancer in women. These two models are K-NNs and Random Forest. In conclusion, these supervised classifiers have been trained to detect malignant and benign cells. In the future, this study may be extended for BC classification on medical images on larger dataset in order to diagnose cancer at early stages.

Journal of Biomedical Science and Engineering, 2013

As the incidence of this disease has increased significantly in the recent years, expert systems and machine learning techniques to this problem have also taken a great attention from many scholars. This study aims at diagnosing and prognosticating breast cancer with a machine learning method based on random forest classifier and feature selection technique. By weighting, keeping useful features and removing redundant features in datasets, the method was obtained to solve diagnosis problems via classifying Wisconsin Breast Cancer Diagnosis Dataset and to solve prognosis problem via classifying Wisconsin Breast Cancer Prognostic Dataset. On these datasets we obtained classification accuracy of 100% in the best case and of around 99.8% on average. This is very promising compared to the previously reported results. This result is for Wisconsin Breast Cancer Dataset but it states that this method can be used confidently for other breast cancer diagnosis problems, too.

2012 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom), 2012

The Concept of classification and learning will suit well to medical applications, especially those that need complex diagnostic measurements. Therefore classification technique can be used for cancer disease prediction. This approach is very much interesting as it is part of a growing demand towards predictive diagnosis. From the available studies it is evident that classification and learning methods can be used effectively to improve the accuracy of predicting a disease and its recurrence. In the present work classification techniques namely Support Vector Machine [SVM] and Random Forest [RF] are used to learn, classify and compare cancer disease data with varying kernels and kernel parameters. Results with Support Vector Machines and Random Forest are compared for different data sets. The results with different kernels are tuned with proper parameters selection. Results are analyzed with confusion matrix.