Breast cancer classification using machine learning techniques: a comparative study

Background: The second leading deadliest disease affecting women worldwide, after  lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed.  These approaches show  their effectiveness in data classification in many fields, especially in healthcare.      Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification.  The used dataset is  Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave  the best results with an accuracy of 98% for breast cancer classification.       Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are  performant  ( good accuracy specificity and sensitivity) compared to the  other evaluated algorithms

Training feedforward neural network using genetic algorithm to diagnose left ventricular hypertrophy

In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the ECG signal using windowed filtering technique. These features were applied to the conventional classifier and also to the neural network classifier with the modified weights using a genetic algorithm. The weights were modified by combining the crossover operators such as crossover arithmetic and crossover two-point operator. The results were compared with the various classifiers and the performance of the neural network with the modified weights using a genetic algorithm is outperformed. The accuracy of the weights modified feedforward neural network is 97.5%

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis

Tactile imaging : the requirements to transition from screening to diagnosis of breast cancer - a concise review of current capabilities and strategic direction

This paper presents a review of Tactile Imaging, a developing technology for breast cancer screening finding traction in the marketplace. The paper identifies the necessary steps required to develop the technology from a screening method to the point where stand-alone diagnosis of suspected breast lesions can be performed without the need for a secondary care referral for a mammogram or biopsy. The relevant literature on Tactile Imaging is reviewed and current capabilities in academia are compared with those implemented in industry before being cross referenced with the metrics for breast cancer diagnosis. Tactile Imaging in academia has been shown to be capable of binary lesion classification and has seen extensive development, to where benign biopsy rates could be reduced by 23%. This has not been mirrored in the marketplace however, where market inertia relegates such systems to early warning screening only as an adjunct to mammography. Additionally, for detailed subclass diagnosis of breast conditions, more metrics are required than is currently available from Tactile Imaging at present. A detailed scheme of work is provided to achieve this. The additional metrics required for stand-alone diagnostics using Tactile Imaging are: background breast elasticity, lesion position on the breast, and lesion depth. These can estimate the lesion constituents and thus the histological diagnosis

Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely

Smart COVID-19 Prediction System Using Neural Network

The pandemic of coronavirus COVID-19 has created a great danger and concern for humanity. Many researchers have done different types of work in this area to provide medical services. In this paper, we proposed a smart Covid-19 diagnosis system by using a Feed Forward Backpropagation Neural Network (FFBNN) and Probabilistic Neural Network (PNN). Based on personal information from patients such as (age, gender, contact with sick person) and five symptoms (headache, fever, cough, sore throat, and shortness of breath) for this purpose we used 510 samples that are collected from different sources, and then compared to previous studies. Results of this work showed that using FFBNN has achieved highest accuracy (98.0%), sensitivity (100%), specificity (94.4%), precision (97.1%), recall (100%) and F1-score (98.52%). But PNN that has accuracy, sensitivity, specificity, precision, recall, F1-score of 90.2%, 92.7%, 87.2%, 89.47%, 92.7% and 91.07% respectively. The most relevant features to positive Covid-19 were fever, shortness of breath, and cough with correlation coefficient of 0.591, 0.495 and 0.488