# A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis
## 2 Related Works
Supervised learning-decision tree, random forest, multilayer perception, support vector machine, and linear regression were compared in both the classification and regression categories. The results revealed that under the classification algorithm, the support vector machine provides high accuracy; however, under the regression methodology, multilayer perception regression delivers reduced errors. To accurately diagnose breast cancer in its early stages, it was recommended in [23] that better machine-learning models should be developed. Using a dataset of repetitive blood investigations with anthropometric dimensions for breast cancer diagnosis, the performance of several machine-learning methods was compared. A feature selection technique called neighborhood component analysis was utilized to recognize useful biomarkers for breast cancer prediction. Comparing the effectiveness of the various classifier models, two distinct data partitioning strategies were utilized. All classifiers were optimized using a Bayesian optimization technique, which resulted in the highest possible prediction precision. The effectiveness of each classifier was evaluated using a variety of systems of measurement. The results demonstrated that k Nearest Neighbor, which is based on Bayesian optimization, outperformed the other methods of machine learning tested using the hold-out data division procedure, with an accuracy of 95.833%. The findings provide fresh insight into the potential of enhanced methods for the early diagnosis of breast cancer. Diagnosis and detection of breast cancer were suggested in a comparison study employing data visualization and machine-learning methods [23]. Several machine-learning strategies for detecting breast cancer were suggested in this paper. The results acquired using the logistic regression model with all features involved demonstrated classification accuracy (98.1%) and the strategy exhibited an improvement in accuracy. Because the selection is carried out in tandem with the model tuning process, it typically results in a middle ground between the two feature selection techniques [24]. The most frequent feature selection methods of this type are Lasso and Ridge regression, while a decision tree develops a model utilizing a variety of feature selection approaches. \"Classifiers\" and \"Meta Classifiers\" are components of each nested ensemble classifier. There can be more than two classification methods utilized in a meta-classifier. The nested ensemble classifiers with two layers were created by them. Typically, meta-classifiers use a layered, nested ensemble of classifiers, with two or three distinct classification algorithms per layer. The Wisconsin Dataset was used in the experiments and the k-fold cross-validation method was utilized to evaluate the model's performance. Classification accuracy, precision, recall, F1 measure, ROC, and computing times were compared between the proposed two-layer nested ensemble classifiers and those of individual classifiers (Bayes Net and naive Bayes). The results specified that the suggested two-layer nested ensemble models were superior to both single classifiers and prior attempts. Both the SVM-naive Bayes-Meta Classifier and the SV-BayesNet3-MetaClassifier performed at 98.07 percent accuracy (K = 10). Alternatively, the SV-naive Bayes3-MetaClassifier is more productive because it requires less time to create the model. Early breast cancer detection with machine-learning algorithms can effectively categorize breast growth as benign or malignant [25]. The dataset used was the Wisconsin Breast Cancer Dataset. This dataset was split into two parts, with 65 percent being training data and 35 percent being testing data. A comparative analysis of various classification algorithms was conducted to determine the optimum strategy. It was discovered that parameter selection is quite crucial in the classification process. According to the findings, the random forest classifier produces the best results with 96.5 percent accuracy.