# A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis
## 2 Related Works
All top-tier machine-learning classifiers were used on a variety of data profiles [26]. Linear-SVM applied to the data profile with features fused from the clinical, gene expression, CNA, and CNV datasets yielded the highest accuracy (88.36%) for IntClust subtyping, with Jaccard and Dice scores of 0.802 and 0.8835, respectively. However, when using Linear-SVM and E-SVM classifiers on a variety of data profiles that incorporate features from histopathology pictures, an accuracy of 97.1% was attained, with Jaccard scores ranging from 0.9439 to 0.9472, and a Dice score of 0.971. In conclusion, our research adds credence to the claim that fusing features from multiple METABRIC datasets can enhance the performance of classifiers used to categorize breast cancer subtypes. Histopathological pictures also show promise in identifying Pam50 subtypes, and their use is anticipated to boost the accuracy of IntClust subtyping when applied to a larger population. Histopathological images as biological data are particularly scarce, contributing to the widespread perception of imbalance in breast cancer data [27]. Recent research has shown that the cascade Deep Forest ensemble model, by learning hyper-representations using cascade ensemble decision trees, can outperform other alternatives, such as the general ensemble learning methods and the conventional deep neural networks (DNNs), for imbalanced training sets. In this study, we use a cascading deep forest to classify breast cancer subtypes, IntCluster and Pam50, with a variety of multi-omics datasets and settings. Accuracy was determined to be 83.45% for five subtypes and 77.55% for 10. This work is significant because it demonstrates that the cascade deep forest classifier can be used with only gene expression data to achieve accuracy on par with other techniques that have better computational performance, with times of about 5 s for 10 subtypes and 7 s for five subtypes recorded. Using medical screening and diagnostics, a breast cancer prediction model was established [28]. LDA extracts useful information to avoid overfitting and improve prediction accuracy. This reduces the dataset's features. The proposed approach can develop new features from old ones and then remove them. The newly developed features summarized the original set's information. LDA is beneficial for determining if a set of features can predict breast cancer. The LDA-SVM prediction model uses a support vector machine (SVM) for reliable prediction. The suggested model achieves 99.2% accuracy, 98.0% precision, and 99.0% recall when tested on the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset from the University of California-Irvine machine learning repository. When paired with feature extraction, SVM efficiently handles classification challenges. A multi-modular breath analysis system with machine learning to detect cancer-specific breath from sensor response curves (taxonomies of clusters) was used [29]. They evaluated 54 gastric cancer patients' breaths and 85 controls. An analyzer with gold nanoparticles and metal oxide sensors was used. Curve forms and other comparative features were employed to examine sensor responses. These features were used to train naive Bayes classifiers, SVMs, and random forests. The trained models' accuracy was 77.8% (sensitivity: 66.54%; specificity: 92.39%). Shape-based characteristics enhanced accuracy and sensitivity in most circumstances. This data analysis technique seems to have the potential for detecting stomach cancer-specific breath. The cluster taxonomy-based detector effective range modeling improves outcomes. Machine-learning approaches can predict and diagnose breast cancer early and have become a research focus [30]. This study implemented five machine-learning algorithms: support vector machine (SVM), random forest, logistic regression, decision tree (C4.5), and k Nearest Neighbour (kNN) to the Breast Cancer Wisconsin Diagnostic Dataset, and then compared their performance.