Wisconsin Breast Cancer Dataset Research Articles

Threshold Filtering for Detecting Label Inference Attacks in Vertical Federated Learning

Federated learning, as an emerging machine-learning method, has received widespread attention because it allows users to train locally during the training process and uses relevant cryptographic knowledge to safeguard the privacy of data during model aggregation. However, existing federated learning is also susceptible to privacy breaches, e.g., label inference attacks against vertical federated learning scenarios, where an adversary is able to reason about the labels of other participants based on the trained model, leading to serious privacy breaches. In this paper, we design a detection method for label inference attacks in vertical federated learning scenarios, which is able to detect the attacks based on the principles of the attacks. We design a threshold-filtering detection method based on the principle of attack to determine that the model is under attack when the threshold value is greater than a set parameter. Furthermore, we have created six threat model classifications based on different a priori conditions of the adversary to comprehensively analyze the adversary’s attacks. In addition to the detection method of attacks, the extent of attacks on the model and the effectiveness of the defense can also be evaluated. The evaluation module will experimentally measure the changes in the relevant metrics such as the accuracy of the attack, the F1 score, and the change in the accuracy after the defense method. For example, detection in the full connected neural network model assesses the attack and defense effectiveness of the model with an attack accuracy of 86.72% in the breast cancer Wisconsin dataset and an F1 score of 0.743, which is reduced to 36.36% after dispersed training. This ensures that users have an overall grasp of the extent to which the training model is under attack before deploying the model.

Enhancing Efficacy in Breast Cancer Screening with Nesterov Momentum Optimization Techniques

In the contemporary landscape of healthcare, machine learning models are pivotal in facilitating precise predictions, particularly in the nuanced diagnosis of complex ailments such as breast cancer. Traditional diagnostic methodologies grapple with inherent challenges, including excessive complexity, elevated costs, and reliance on subjective interpretation, which frequently culminate in inaccuracies. The urgency of early detection cannot be overstated, as it markedly broadens treatment modalities and significantly enhances survival rates. This paper delineates an innovative optimization framework designed to augment diagnostic accuracy by amalgamating momentum-based optimization techniques within a neural network paradigm. Conventional machine learning approaches are often encumbered by issues of overfitting, data imbalance, and the inadequacy of capturing intricate patterns in high-dimensional datasets. To counter these limitations, we propose a sophisticated framework that integrates an adaptive threshold mechanism across an array of gradient-based optimizers, including SGD, RMSprop, adam, adagrad, adamax, adadelta, nadam and Nesterov momentum. This novel approach effectively mitigates oscillatory behavior, refines parameter updates, and accelerates convergence. A salient feature of our methodology is the incorporation of a momentum threshold for early stopping, which ceases training upon the stabilization of momentum below a pre-defined threshold, thereby pre-emptively preventing overfitting. Leveraging the Wisconsin Breast Cancer Dataset, our model achieved a remarkable 99.72% accuracy and 100% sensitivity, significantly curtailing misclassification rates compared to traditional methodologies. This framework stands as a robust solution for early breast cancer diagnosis, thereby enhancing clinical decision making and improving patient outcomes.

Medical Datasets Training and Enhancing Disease Prediction Accuracy

Data mining is an effective method that uses sophisticated tools and strategies to sift through massive databases in search of useful patterns. Its usefulness extends to many fields, including medicine. We used AdaBoost, Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF) as predictive models in our investigation (ADaB). Evaluating utilizing methods like cross-validation and random sampling allowed us to concentrate on improving accuracy. Medical datasets were used to construct the following: Heart Disease (HD) dataset, Breast Cancer Wisconsin (BCW) dataset, and Covid-19 dataset. We set out to improve the accuracy of disease predictions and push the boundaries of medical data analysis. After completing the assessment procedure with the training data, the performance measurements showed that the highest accuracy was achieved with LR 83% for HD, KNN 97%, and LR 98% for Covid-19.

Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques

Introduction: One of the most common types of cancer and a significant contributor to the high death rates among women is breast cancer. It usually occurs in women. It is crucial to acquire a diagnosis early in order to kill cancer from becoming worse. Objective: The traditional diagnosing procedure takes more time. A fast and useful option can apply Machine Learning Technique (MLT) to identify illnesses. However new technology creates a variety of high-dimensional data kinds particularly when it comes to health or cancer data. Methods: Data classification techniques like Machine Learning are efficient. Particularly in the medical field where such techniques are often utilised to make decisions via diagnosis and analysis. Using Wisconsin Breast Cancer Dataset, the proposed research was carried out (WBCD). Some of these issues may be solved using the feature selection approach. Results: This research analyses the classification accuracy of different MLT: Logistic Regression, Support Vector Machine, and K-Nearest Neighbour. According to experiment results, SVM has the best accuracy of all algorithms, at 97.12%. Conclusion: The mentioned prediction models are based on several supervised MLT. Tenfold cross validation is applied. Additionally, author also proposed a Flow chart of breast Cancer using MLT.

An Efficient Gradient Based Modified Cuckoo Search Optimization with Dynamic Levy Flight (MCSO-DLF) Algorithm for Breast Cancer Detection

Purpose of Study: This study evaluates the Gradient-Based Dynamic Levy Flight Cuckoo Search Optimization (MCSO-DLF) for breast cancer detection and compares it to six established optimization algorithms. The goal is to determine if MCSO-DLF offers higher accuracy and computational efficiency for optimizing diagnostic models. Methodology: The study compares MCSO-DLF with PSO, GA, ACO, SA, DE, and ABC using the Breast Cancer Wisconsin dataset. The algorithms are evaluated based on accuracy, convergence speed, and efficiency. MCSO-DLF uses dynamic Levy flight and gradient-based optimization to improve solution quality. Main Findings: MCSO-DLF achieved the highest accuracy (0.9912) and fastest computation time (65.94 seconds), outperforming all other algorithms. This demonstrates its effective balance between exploration and local solution refinement. Implications: MCSO-DLF significantly improves accuracy and speed, offering potential advancements in breast cancer detection systems, leading to better patient outcomes and more efficient healthcare. Novelty of Study: This study introduces MCSO-DLF as a novel hybrid optimization method that combines gradient-based optimization with dynamic Levy flight, outperforming traditional algorithms in complex medical diagnostics.

An Efficient Gradient Based Modified Cuckoo Search Optimization with Dynamic Levy Flight (MCSO-DLF) Algorithm for Breast Cancer Detection

Purpose of Study: This study evaluates the Gradient-Based Dynamic Levy Flight Cuckoo Search Optimization (MCSO-DLF) for breast cancer detection and compares it to six established optimization algorithms. The goal is to determine if MCSO-DLF offers higher accuracy and computational efficiency for optimizing diagnostic models. Methodology: The study compares MCSO-DLF with PSO, GA, ACO, SA, DE, and ABC using the Breast Cancer Wisconsin dataset. The algorithms are evaluated based on accuracy, convergence speed, and efficiency. MCSO-DLF uses dynamic Levy flight and gradient-based optimization to improve solution quality. Main Findings: MCSO-DLF achieved the highest accuracy (0.9912) and fastest computation time (65.94 seconds), outperforming all other algorithms. This demonstrates its effective balance between exploration and local solution refinement. Implications: MCSO-DLF significantly improves accuracy and speed, offering potential advancements in breast cancer detection systems, leading to better patient outcomes and more efficient healthcare. Novelty of Study: This study introduces MCSO-DLF as a novel hybrid optimization method that combines gradient-based optimization with dynamic Levy flight, outperforming traditional algorithms in complex medical diagnostics.

Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis.

Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset with a particular emphasis on finding which models perform the best for breast cancer diagnosis. The study also explores the contrast between the proposed ANN methodology and conventional machine learning techniques. The comparison between the methods employed in the current study and those utilized in earlier research on the Wisconsin Breast Cancer dataset is also compared. The findings of this study are in line with those of previous studies which also highlighted the efficacy of SVM, Decision Tree, CART, ANN, and ELM ANN for breast cancer detection. Several classifiers achieved high accuracy, precision and F1 scores for benign and malignant tumours, respectively. It is also found that models with hyperparameter adjustment performed better than those without and boosting methods like as XGBoost, Adaboost, and Gradient Boost consistently performed well across benign and malignant tumours. The study emphasizes the significance of hyperparameter tuning and the efficacy of boosting algorithms in addressing the complexity and nonlinearity of data. Using the Wisconsin Breast Cancer (original) dataset, a detailed summary of the current status of research on breast cancer diagnosis is provided.

Optimizing Breast Cancer Diagnosis with Machine Learning Algorithms

Breast cancer is the most common disease among women worldwide, and it continues to pose a serious threat to global health [2]. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods, while effective, have limitations in consistency and speed. Medical diagnostics have undergone a revolution with the introduction of machine learning (ML), which provides tools to analyze complex data and increase diagnosis accuracy. Using the Breast Cancer Wisconsin (Diagnostic) Dataset[1], this investigation dives into the application of several machine learning (ML) algorithms to improve the effectiveness of the diagnosis of breast cancer in terms of accuracy. Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Support Vector Classifier (SVC), XGBoost Classifier (XGBC), and Convolutional Neural Network (CNN) are used. Improved performance metrics were obtained across models by applying optimization approaches, particularly genetic algorithm for the selection of features, following preprocessing and initial training. RandomForestClassifier, XGBClassifier, and CNN notably showed significant enhancements in accuracy, ROC AUC score, recall, precision, and F1 score post-optimization. Ensemble methods and deep learning architectures proved effective in handling complex data and reducing overfitting, with Random Forest Classifier standing out as consistently superior. Conversely, Decision Tree Classifier and Support Vector Classifier exhibited mixed results, showcasing the importance of optimization strategies. This research shows the capability of ML in augmenting breast cancer diagnostics, advocating for further exploration of ensemble methods and advanced neural networks to refine diagnostic tools and improve patient outcomes.

Artificial Intelligence (AI) and Nuclear Features from the Fine Needle Aspirated (FNA) Tissue Samples to Recognize Breast Cancer.

Breast cancer is one of the paramount causes of new cancer cases worldwide annually. It is a malignant neoplasm that develops in the breast cells. The early screening of this disease is essential to prevent its metastasis. A mammogram X-ray image is the most common screening tool practiced currently when this disease is suspected; all the breast lesions identified are not malignant. The invasive fine needle aspiration (FNA) of a breast mass sample is the secondary screening tool to clinically examine cancerous lesions. The visual image analysis of the stained aspirated sample imposes a challenge for the cytologist to identify the malignant cells accurately. The formulation of an artificial intelligence-based objective technique on top of the introspective assessment is essential to avoid misdiagnosis. This paper addresses several artificial intelligence (AI)-based techniques to diagnose breast cancer from the nuclear features of FNA samples. The Wisconsin Breast Cancer dataset (WBCD) from the UCI machine learning repository is applied for this investigation. Significant statistical parameters are measured to evaluate the performance of the proposed techniques. The best detection accuracy of 98.10% is achieved with a two-layer feed-forward neural network (FFNN). Finally, the developed algorithm's performance is compared with some state-of-the-art works in the literature.

Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach

<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>

Breast Cancer Disease Prediction Using Random Forest Regression and Gradient Boosting Regression

The current research endeavors to evaluate the efficacy of regression-based machine learning algorithms through an assessment of their performance using diverse metrics. The focus of our study involves the implementation of the breast cancer Wisconsin (Diagnostic) dataset, employing both the random forest and gradient-boosting regression algorithms. In our comprehensive performance analysis, we utilized key metrics such as Mean Squared Error (MSE), R-squared, Mean Absolute Error (MAE), and Coefficient of Determination (COD), supplemented by additional metrics. The evaluation aimed to gauge the algorithms' accuracy and predictive capabilities. Notably, for continuous target variables, the gradient-boosting regression model emerged as particularly noteworthy in terms of performance when compared to other models. The gradient-boosting regression model exhibited remarkable results, highlighting its superiority in handling the breast cancer dataset. The model achieved an impressively low MSE value of 0.05, indicating minimal prediction errors. Furthermore, the R-squared value of 0.89 highlighted the model's ability to explain the variance in the data, affirming its robust predictive power. The Mean Absolute Error (MAE) of 0.14 reinforced the model's accuracy in predicting continuous outcomes. Beyond these core metrics, the study incorporated additional measures to provide a comprehensive understanding of the algorithms' performance. The findings underscore the potential of gradient-boosting regression in enhancing predictive accuracy for datasets with continuous target variables, particularly evident in the context of breast cancer diagnosis. This research contributes valuable insights to the ongoing exploration of machine learning algorithms, providing a basis for informed decision-making in medical and predictive analytics domains.

Feature Selection using Extra Trees for Breast Cancer Prediction

Breast cancer is a disease that seriously threatens women's health. It is considering a common death cause in women. Machine learning has made significant progress in recent years to improve the effectiveness of early diagnosis of various diseases. Accurate predication and detection help decrease the death rate of breast cancer. This paper aims to predict breast cancer using several machine-learning techniques. The idea is to lower the number of features in the Wisconsin Breast Cancer Dataset (WCDB) and use it for prediction. The study used the extra trees method for feature selection and Random forest, Logistic regression, and Support Vector Machine (SVM) for testing the dataset. According to the results, SVM achieved the best performance among the other models with 98% accuracy. The proposed method in this study proved its effectiveness in breast cancer prediction.

Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis.

Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system's performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study's findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.

Breast Cancer Classification using XGBoost

Breast cancer continues to be one of the foremost illnesses that results in the deaths of numerous women each year. Among the female population, approximately 8% are diagnosed with Breast cancer (BC), following Lung Cancer. The alarming rise in fatality rates can be attributed to breast cancer being the second leading cause. Breast cancer manifests through genetic transformations, persistent pain, alterations in size, color (redness), and texture of the breast's skin. Pathologists rely on the classification of breast cancer to identify a specific and targeted prognosis, achieved through binary classification (normal/abnormal). Artificial intelligence (AI) has been employed to diagnose breast tumors swiftly and accurately at an early stage. This study employs the Extreme Gradient Boosting (XGBoost) machine learning technique for the detection and analysis of breast cancer. XGBoost provides an accuracy of 94.74% and recall of 95.24% on Wisconsin breast cancer Wisconsin (diagnostic) dataset.

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms.

The incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community. This study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant. Our research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm. The results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques. The constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.

Breast Cancer Prediction: A Fusion of Genetic Algorithm, Chemical Reaction Optimization, and Machine Learning Techniques

Breast cancer is currently one of the most prevalent cancers affecting women globally. Uncontrolled growth and division of breast cells lead to the formation of tumors, marking the onset of breast cancer. Predicting breast cancer is essential for early detection, making treatment plans, and implementing preventive measures, ultimately improving patient outcomes and reducing mortality rates. In recent years, numerous studies have been published to predict breast cancer where researchers use a variety of methods. Most investigations have been conducted using narrow and specific datasets, often resulting in a lack of accuracy. Such methods may not be suitable for clinical use. The study aims to address the limitations of existing models in terms of robustness and generalization across diverse datasets. In our study, we employed two metaheuristic algorithms, namely, genetic algorithm (GA) and chemical reaction optimization (CRO) with machine learning techniques, including support vector machine (SVM), decision tree, random forest, and XGBoost. GA and CRO are used to optimize the feature selection process. It enables machine learning algorithms to predict more accurately. Experiments were conducted on three datasets, namely, Wisconsin Breast Cancer (WBC), Breast Cancer‐the University of California, Irvine (BC‐UCI), and Breast Cancer Coimbra (BCC) datasets. The datasets contain 569, 286, and 116 instances, respectively. The classifiers with optimized features consistently outperformed the classifiers without feature optimization in terms of accuracy, precision, recall, specificity, and F1 score. Among the compared methods published recently, our method attained the highest accuracies of 99.64% in the WBC dataset and 98% in the BCC dataset, as well as the second highest accuracy of 99.12% in the BC‐UCI dataset. Comparative analysis demonstrated the superiority of our approach over existing methods.

Evaluation of Adaptive Synthetic Resampling Technique for Imbalanced Breast Cancer Identification

As one of the most common types of cancer among women, breast cancer is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. However, detecting breast cancer is challenging due to imbalanced classification, where the minority class (cancerous) is ominously smaller than the majority class (non-cancerous). In this paper, we explore the use of logistic regression (LR) and the adaptive synthetic resampling (ADASYN) technique to address imbalanced classification in breast cancer detection. To that end, we collected the Wisconsin Breast Cancer dataset, which contains 569 instances. The dataset is imbalanced, with 212 malignant (cancerous) cases and 357 benign (non-cancerous) cases. Then, we trained support vector machine, LR, K-nearest neighbor, gradient, and adaptive boosting on the imbalanced dataset. Finally, we trained these algorithms on resampled data with the ADASYN oversampling and we evaluated their performance using cross-validation score with 5-folds. The results of the experiment showed that using ADASYN with LR significantly improved the performance the LR model. The LR model achieves 99.46% accuracy on breast cancer diagnosis. Moreover, the confusion matrix shows that among the 188 samples, the model misclassified one cancerous instance. Thus, we concluded that the proposed model is effective for breast cancer diagnosis.

Clinical Dataset Classification Using Feature Ranking And Satin Bower Bird Optimized SVMs

Abstract A clinical decision support system is a computer-based system that is designed to assist healthcare providers with clinical decision-making by analyzing electronic health records and other healthcare information systems to provide real-time support to clinicians at the point of care. A novel classification framework for clinical datasets in which the relevant features are selected by ranking them based on Fisher’s Score and a wrapper-based Satin Bower Bird Optimization algorithm with the combination of accuracy, G-mean and F-Score measured by support vector machine (SVM) as the fitness function is proposed. The classification is performed using an SVM classifier in which the hyperparameters of the SVM classifier are optimized using the Satin Bower Bird Optimization that improves the classification performance. In the context of statistical analysis, the research undergoes a non-parametric Friedman Test. This study selects relevant attributes from three clinical datasets from the Machine Learning Repository maintained by the University of California Irvine and achieved an accuracy of 86% for the Breast Cancer Wisconsin (Diagnostic) dataset, 89% for the Diabetic Retinopathy Debrecen dataset, and 91% for the EEG Eye State dataset respectively. When compared with other machine learning classifiers the proposed approach performed well with feature selection compared with other machine learning classifiers.

Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence

With more than 2.1 million new cases of diagnosis each year, breast cancer is considered to be the most prevalent women disease. Within 10 years, nearly 30% patients who got cured at early-stages experienced cancer recurrence. Recurrence is a crucial aspect of breast cancer behaviour that is inseparably linked to mortality. Despite its importance, the significant proportion of breast cancer datasets rarely include it, which makes research into its prediction more challenging. It is still difficult to predict who will experience a recurrence and who won't, which has implications for the treatment that goes along with it. Clinicians treating breast cancer may be able to avoid ineffective overtreatment if Artificial Intelligence (AI) methods are developed that can forecast the likelihood of breast cancer recurrence. This work proposes a novel automatic breast cancer recurrence classification and prediction system incorporating novel Deep Convolutional Neural Network (DCNN) algorithm. The proposed DCNN model is deployed on Wisconsin Breast Cancer dataset for further evaluation. The role of AI in forecasting recurrence is examined in this work. The experimental results were analysed for various combination of train and validation dataset. The accuracy, precision, recall and F1-score for the proposed DCNN was calculated as 97.63 %, 98.57 %, 96.84 %, 97.89 % respectively.

A study on the crucial indicators for breast cancer detection using machine learning algorithm

Since breast cancer is the most serious disease affecting women, early detection comes as a priority. The Wisconsin Breast Cancer Dataset (WBCD), which was retrieved from the UCI database, has been applied in numerous studies in recent years to help with the definitive diagnosis. Machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Neural Network (NN) can be used to attain the upshot. Although these algorithms make predictions well, advantages cannot overshadow drawbacks because the outcome is circumstantial by the peculiar dataset itself and cannot draw a direct conclusion reflecting the deeper issue. To implement ML skills to figure out the factors that influence the prediction most in a statistical dimension, this paper uses the dataset above, compares five methods, and chooses three best classifiers: KNN, RF, and SVM. After selection, the author eliminates every single variable each time to get the accuracy, and compares them with the full model’s accuracy. Having controlled variables, it can be informed that Clump Thickness and Bare Nuclei are the factors that matter most.