Analysis of Breast Cancer Classification with Machine Learning based Algorithms

Breast cancer is the most incident cancer worldwide. The evaluation of molecular subtypes and their biomarkers plays an essential role in prognosis. The biomarkers used are Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal growth factor Receptor-type 2 (HER2), and Ki67. Based on these, subtypes are classified as Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). The gold standard for this analysis is histology and immunohistochemistry, semi-quantitative techniques that present inter-laboratory and inter-observer variations. The Fourier Transform Infrared micro-spectroscopy (micro-FTIR), which provides hyperspectral images with biochemical information of biological tissues, is applied together with artificial intelligence (AI) for cancer evaluation. In this thesis, twenty samples of two breast cancer cell lines, BT-474 and SK-BR-3, were used to define the optimal number of co-added scans for machine learning (ML) techniques. Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB) models were used. Sixty hyperspectral images of 320x320 pixels were collected from thirty patients of a human breast biopsies microarray, each containing a breast cancer (CA) and an adjacent tissue (AT) core. Automated methods based on K-Means clustering were developed for data organization and pre-processing to one-dimensional (1D) and two-dimensional (2D) data. The dataset was used to train two new deep learning models for breast cancer subtype evaluation: CaReNet-V1, a 1D Convolutional Neural Network (CNN); and CaReNet-V2, a 2D CNN. All ML models achieved similar performances with the b256_064 (256 background scans and 64 sample scans), b256_128, and b128_128 groups, where the best accuracy of 0.995 was presented by the XGB model. The b256_064 was established as the ideal among the three due to the shortest acquisition time. The K-Means-based method enabled fully automated preprocessing and organization, improving data quality and optimizing CNN training. CaReNet-V1 effectively classified CA and AT (individual spectra test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and 0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabled the evaluation of the most contributing wavenumbers to the predictions, providing a direct relationship with the biochemical content of the samples. CaReNet-V2 demonstrated better performance than 1D, with test accuracies above 0.84, and enabled the prediction of ER, PR, and HER2 levels, where borderline values showed lower performance (minimum accuracy of 0.54). The Ki67 percentage regression demonstrated an absolute mean error of 3.6%. On the other hand, its impact evaluation by wavenumber was inferior to 1D. Thus, this study indicates image-based AI techniques using micro-FTIR as potential providers of additional information to pathological reports, also serving as patient biopsy screening techniques.

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.

The problems of plant leaf disease are rather serious in the world agricultural industry, leading to a significant decrease in crop quantity and quality, consequently, resulting in a huge loss in the economy and food insecurity. Detection and successful classification of plant diseases at the initial stage is essential to further agricultural output and the quality production of food. The recent improvements in the field of artificial intelligence (AI), specifically, machine learning (ML) and deep learning (DL), have shown significant prospects in automating and enhancing methods of diagnosing plant leaf diseases by using a wide variety of ML and DL algorithms. This review article presents an in-depth analysis of thirty novel methods created by researchers to diagnose and classify plant leaf diseases. They are such conventional classifiers as Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN), along with some advanced DL architectures, such as Convolutional Neural Networks (CNN), VGG16, ResNet50, InceptionResNetV2, EfficientNet, and various hybrids. The review analysis takes into consideration the methodologies applied, performance metrics, and insights in practice, as well as the strengths and weaknesses of both. The most crucial findings of the review show that deep learning models, and CNNs in particular, tend to be more accurate, robust, and feature extractors than traditional models of MLs. The performance of classification is also enhanced by numerous hybrid models that will use ML together with DL, and transfer learning has been an effective method to enhance the generalization using small datasets. Nevertheless, with all this progress, the issues of diversity of datasets, computational resource requirements and model interpretability are still to be explored in the future.

Significant advancements have been achieved recently in the use of artificial intelligence (AI) to solve healthcare-related problems. A potential method for identifying and treating many illnesses is machine learning (ML). The “Breast Cancer Dataset” was used in this work to perform classification tasks using ML techniques including Support Vector Machine (SVM), Random Forest (RF) and MultiLayer Perceptron (MLP) for identifying the forms of breast cancer, either Benign or Malignant. The SVM, RF, and MLP achieved impressive PR scores exceeding 0.98. However, MLP exhibited superior performance among these algorithms, achieving an exceptional AUC value of 99.71. Conversely, SVM slightly outperformed the other algorithms in terms of training accuracy, with a value of 97.90%. This work used shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) to shed light on how these categorization algorithms made judgments. These interpretability methodologies showed that the majority of characteristics consistently predicted the presence of malignant or benign cancer types. This study demonstrates how ML algorithms' decision-making procedures may be understood, possibly promoting the use of ML in clinical settings for medical diagnosis.

The major consequence of breast cancer is death. It has been proven in many studies that machine learning techniques are more efficient in diagnosing breast cancer. These algorithms have also been used to estimate a person's likelihood of surviving breast cancer. In this study, we employed machine learning algorithms to predict breast cancer. A total of 569 breast cancer datasets were obtained from kaggle sites. Some of the machine learning algorithms that we use are K-Nearest Neighbor (KNN), besides Random Forest (RF), there is also Gradient Boosting (GB), then Gaussian Naive Bayes (GNB), Vector Support Machine (SVM), and then Logistic Regression (LR). Before algorithms were used to train and test breast cancer datasets, StandardScaler was leveraged to transform training datasets and test datasets for improved algorithm performance. As a result of this utilization, the performance measurement carried out succeeded in producing high accuracy. The highest results were obtained from the Logistic Regression algorithm with an accuracy value of 99%. The value of precison is 99% benign, and 100% malignant. The recall results are 100% benign, and 98% malignant. The F1-Score results show 99% benign, and 99% malignant. It is hoped that this research can help the medical party to determine the next step in dealing with breast cancer.

<p>Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.</p>

Machine Learning Techniques for Breast Cancer Prediction

The most severe disease nowadays is breast cancer. The abnormal development of breast cells is what causes breast cancer. The breast ducts may include this type of malignancy. Women over 40 are the ones who get diagnosed with breast cancer most frequently. Women over 50 have the highest incidence (about 77% of invasive cases). A clear understanding of breast cancer and its kinds, as well as the creation of prophylactic measures, have made significant strides. Many genes have been linked to breast cancer. Malignant and benign cells both fall within this category. Cancerous malignant conditions exist. Non-cancerous benign cancer. Malignant cells affect other cells of the body as well as the tissues continue to divide uncontrollably. While benign tumors are significantly less dangerous than malignant ones since they do not spread to other parts of the body. Treatment is not necessary for these cancers. Malignant tumors can be treated with chemotherapy, radiation therapy, and immunotherapy, among other forms of treatment. The algorithms used to develop this prediction system are machine learning-based. Machine learning became a research focus and was found to be an effective method. Recently, many scholars have become interested in the developing technique of machine learning. Scikit-learn is used to create a model that categorizes breast cancer as either malignant or benign. A type of library that is included with the collab platform is called Scikit-learn. Mammograms have historically been the primary method of finding breast cancer. Any calcium deposits or calcifications in the breasts might be seen on a mammogram. Considering how pricey it is, few people can afford it. It will be great if an early diagnosis occurs so that treatment can get underway right away. Many women's lives could be saved by this. This dataset analysis will benefit from the use of machine learning algorithms. Six machine learning (ML) methods are compared in this paper: Naive Bayes (NB), Random Forest (RT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT). These methods will be applied to accurately forecast outcomes. Due to early detection, the 5-year survival rate for breast cancer patients is above 82 %. The eventual goal of this project is to assist clinicians in analyzing enormous databases of available cancer data and discovering patterns using patient data and that cancer data. This endeavor to a modest stride in the protracted battle against breast cancer.

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.

Recently, there has been a remarkable growth in Artificial Intelligence (AI) with the development of efficient AI models and high-power computational resources for processing complex datasets. There has been a growing number of applications of machine learning in satellite remote sensing image data processing. In this work, machine learning methods were applied for crop classification of temporal multi- spectral satellite image to achieve better prediction of crop-wise area statistics. In India, agriculture has a huge impact on the national economy and most of the critical decisions are dependent on agricultural statistics. Sentinel-2 satellite image data for the Guntur district region of Andhra Pradesh, India has been used as the study area. The main reason for selecting this region is the diversity of agricultural crops and the availability of ground truth. As a baseline, the performance of machine learning models like Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbor (KNN) on crop classification was evaluated on 2016 single time multi-spectral satellite image. SVM performed well with an F1 score of 0.94. Further, the performance of SVM, RF, 1D and 2D Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and RNN with Gated Recurrent Unit (GRU) on 2018 Kharif season temporal multi-spectral satellite image has been evaluated for estimation of crop areas. The results show that SVM has the best F1 score of 0.99 and achieved a 95.9% agreement with the ground surveyed crop areas.

Background: The merging of machine learning and deep learning approaches offers a viable route for improving breast cancer prediction in this era of advanced technology and data-driven healthcare. This study focused on the use of supervised machine learning and deep learning algorithms for breast cancer prediction using Wisconsin breast cancer dataset. Methods: The features from this dataset were computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. Supervised machine learning algorithms, such as logistic regression, random forests, and support vector machines were compared with deep learning algorithms like convolutional and recurrent neural networks (RNN and CNN) for the prediction of breast cancer. The techniques combine clinical, genomic, and image-based data, that both aid in early diagnosis and individualized treatment suggestions. Results: The results of model comparison were very promising, with models obtaining excellent accuracy, sensitivity, and specificity in breast cancer diagnosis. The study used a pair plot and different classifiers with their accuracy scores on test dataset to determine which machine learning model was most suitable based on the relationship of variables represented in the plot. Support vector machine (SVM) has the highest accuracy of 98.8% and Artificial Neural Network (ANN) has lowest accuracy of 95.80% using confusion matrix and the accuracy metric. Conclusion: The study revealed that Support Vector Machine (SVM) has the highest prediction capability for Breast Cancer using Supervised Machine Learning and Deep Leaning. The new classifier performed better than the existing breast cancer prediction models in the literature.

Breast cancer (BC) is one of the most deadly cancers for the women across the world. Early detection of breast cancer is very important for life saving. Though it is one of the most critical diseases, yet permanent cure has not been developed till now. Artificial intelligence (AI) and machine learning (ML) have been playing a vital role for effective and quick detection of this disease and increasing the rate of survivals. Deep learning (DL) technologies are helping for the analysis of very important features affecting prediction and detection of serious breast cancer diseases. This research paper focuses on solving the problem of BC detection using Wisconsin Diagnosis Breast Cancer (WDBC) data set by applying different ML models after training and validation. Additionally, various types of performance metrics have been calculated and studied. Various ensemble models have also been developed for improved detection of BC. Recurrent neural network (RNN) and gated recurrent unit (GRU)-based DL models are used for feature extraction and training. Different classification models used in this literature are random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR). After classification, the majority voting and stacking ensemble models have been applied for better performances. After exhaustive simulation and analysis, the performance measures in terms of accuracy, precision, recall, F1-score and area under the curve (AUC) values of the ensembled model are 97.3%, 0.97, 0.971, 0.97 and 0.974, respectively. The proposed ensembled model gives higher performances compared to the individual ML models.

Despite advances in diagnostics, the objective and repeatable assessment of patients with neurological deficits (e.g., stroke) remains a major challenge. Modern methods based on artificial intelligence (AI) are of interest to researchers and clinicians in this area. This study presents a comparative analysis of different AI approaches used to analyze gait of stroke patients using a retrospective dataset of 120 individuals. The main objective is to evaluate the effectiveness, accuracy, and clinical relevance of machine learning (ML) and deep learning (DL) models in identifying gait abnormalities and predicting rehabilitation outcomes. Multiple AI techniques—including support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), and convolutional neural networks (CNN)—were trained and tested on time-series gait data with spatiotemporal parameters. Performance metrics such as accuracy, precision, recall, and area under the curve (AUC) were used to compare model results. Initial results indicate that DL models, particularly CNNs, outperform traditional ML methods in capturing complex gait patterns and providing reliable classification. However, simpler models showed advantages in interpretability and computational efficiency. This study highlights the potential and shortcomings of AI-based gait analysis tools in supporting clinical decision-making and planning personalized stroke rehabilitation.

The fields of artificial intelligence (AI) and machine learning (ML) have attracted significant interest and investment from a diverse range of industries, especially during the last several years. Despite the fact that AI methods have been used extensively and put through extensive testing in the healthcare industry, the recently discovered coronavirus disease (COVID-19) necessitates the use of these methods in order to prevent the emergence of the disease. The proposed system is based on six ML algorithms to predict COVID-19 infection as random forest (RF) algorithm, naive bayes (NB) algorithm, support vector machine (SVM) algorithm, decision tree (DT) algorithm, multi-layer perceptron (MLP), and k-nearest neighbor (KNN). It is based on two steps: first, we uploaded the dataset to train the model. Then, we test our model on those cases to work directly after making a trained classifier so it can directly discover with automatic COVID-19 prediction state of a patient suspected or not. The proposed system results showed the high accuracy of NB, DT, and SVM as 98.646%. Besides the better time to build the model and early predict the state of patients is 31 ms of the NB algorithm.