Prediction of Breast Cancer using Machine Learning

When it comes to cancer in Indian women, breast cancer is one of the most common and frequently occurring cancer types. In most of the developing countries like India, the overhead charges for breast cancer are very high which leads to a poor mortality rate when compared to western countries. As of now, large numbers of researches have been carried out based on deep learning and machine learning prediction of Breast cancer. This paper reviews some of the existing machine learning algorithms like Decision Tree (DT), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machines (SVMs) in linear (SVM-L), polynomial (SVM-P), and radial basis function (SVM-RBF) on the Breast Cancer Wisconsin (BCW) data set using Python. An experimental result shows that SVM-L outperforms all the above algorithms with 96.5% accuracy.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Data analytics play vital roles in diagnosis and treatment in the health care sector. To enable practitioner decisionmaking, huge volumes of data should be processed with machine learning techniques to produce tools for prediction and classification Breast Cancer reports 1 million cases per year. We have proposed a prediction model, which is specifically designed for prediction of Breast Cancer using Machine learning algorithms Decision tree classifier, Naïve Bayes, SVM and K-Nearest Neighbour algorithms. The model predicts the type of tumour, the tumour can be benign (noncancerous) or malignant (cancerous). The model uses supervised learning which is a machine learning concept where we provide dependent and independent columns to machine. It uses classification technique which predicts the type of tumour.

Procedia Computer Science, 2021

Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer Wisconsin Diagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of this research paper is to predict and diagnosis breast cancer, using machine-learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%).All the work i s done in the Anaconda environment based on python programming language and Scikit-learn library.

THE 3RD FACULTY OF INDUSTRIAL TECHNOLOGY INTERNATIONAL CONGRESS 2021 INTERNATIONAL CONFERENCE: Enriching Engineering Science through Collaboration of Multidisciplinary Fields

Algorithms in machine learning are a very important part because the type of algorithm used has an impact on the level of prediction accuracy and classification of a data set that is used. Appropriate use is accompanied by machine learning capabilities, namely being able to study past patterns, making machine learning have an advantage in prediction accuracy which can reach up to 90%. Therefore, machine learning has the opportunity to be an alternative that can avoid diagnostic errors that occur in the case of breast cancer. Breast cancer is one of the highlights of the impact of diagnostic errors because there are 10-30% of cases due to diagnostic errors, thus we need an alternative that can help reduce these diagnostic errors. In this study, an analysis of the logistic regression algorithm was carried out using the python programming language. The evaluation method is very important to know the performance in the prediction process. By using three evaluation methods, namely cross-validation k=10, confusion matrix, and ROC AUC. From the results of this study, it was found that the algorithm Logistic regression has an accuracy of 96.5% and an error of 0.19.

Artificial Intelligence and Applications

Breast cancer is one of the most common diseases that causes the death of several women around the world. So, early detection is required to help decrease breast cancer mortality rates and save the lives of cancer patients. Hence early detection is a significant process to have a healthy lifestyle. Machine learning provides the greatest support to detect breast cancer in the early stage, since it cannot be cured and brings great complications to our health system. In this paper, novel models are generated for prediction of breast cancer using Gaussian Naive Bayes (GNB), Neighbour’s Classifier, Support Vector Classifier (SVC) and Decision Tree Classifier (CART). This paper presents a comparative machine learning study based to detect breast cancer by employing four different Machine Learning models. In this paper, experiment analysis carried out on a Wisconsin Breast Cancer dataset to evaluate the performance for the models. The computation of the model is simple; hence enabling an e...

Breast cancer is the most common cancer in women both in the developed and less developed world. Early detection based on clinical features can greatly increase the chances for successful treatment. Our goal was to construct a breast cancer prediction model based on machine learning algorithms. A total of 10 potential clinical features like age, BMI, glucose, insulin, HOMA, leptin, adiponectin, resistin, and MCP-1 were collected from 116 patients. In this report, most commonly used machine learning model such as decision tree (DT), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models were tested for breast cancer prediction. A repeated 10-fold cross-validation model was used to rank variables on the randomly split dataset. The accuracy of DT, RF, SVM, LR, ANN, and KNN was 0.71, 0.71, 0.77, 0.80, 0.81, and 0.86 respectively. However, The KNN model showed most higher accuracy with area under r...

Machine learning can be the process of developing systems that learn from data to recognize patterns and make accurate predictions of future events in healthcare too. It has considerable potential to transform it to health care. Today the healthcare domain relies more on computer technology. Medical diagnosis is an important task of intelligent systems. Machine learning systems are used to find the abnormalities at an early stage of disease diagnosis. Optimal and accurate diagnosis is a critical factor for identifying appropriate treatment. This study with the importance of Machine Learning systems in healthcare, also focuses on the significance of Machine Learning using Python on the jupyter platform. The application of Machine Learning plays a vital part in public healthcare, the significance of the role of ML in medical data analysis is widely used in many applications. For current study the researcher has used the secondary data of patients. The exploratory analysis of this dataset and creating models for machine learning was the challenge. The other challenge was preprocessing, but fortunately researchers have received the clean data. The machine has been trained and tested and in a later stage the accuracy of the model was tested by making use of a confusion matrix by the research, since the critical task in medical data analysis is the prediction of the accuracy results. For doing so the researcher has made it on one of the diseases, the beast cancer prediction by using Machine Learning using python and which has been proved with 87% accuracy. The future research can be carried out by making use of other Machine Learning algorithms.

Journal of Science Technology and Environment Informatics, 2020

Breast cancer has become a concerning issue in recent years. The rate of women having breast cancer seemed to be increased significantly. The disease has become life-taking if it is not diagnosed at all and in many cases, separation of limbs is the only way to prevent it, if it is diagnosed at the last stage. As a result, a good predictor of this issue can be fruitful in successful diagnosis. The main focus of this paper is to perform different machine learning classification algorithms to correctly predict the target class and improve it by checking the effectiveness of particular attributes of original Wisconsin Breast Cancer dataset (WDBC) for breast cancer diagnosis prediction. After running classifiers on the dataset, the comparison was made among them to find the best performing algorithm and then effective attributes of dataset were analyzed to improve performance further. In this paper, we have used algorithms-Naïve Bayes, Support Vector Machine (SVM), Multilayer Perceptron (MLP), J48 and Random Forest. Here, for comparing the result, we have used performance metrics: Accuracy, Kappa statistic, precision, recall, F-measure, MCC, ROC area, PRC area. Based on the values of performance metrics, Naïve Bayes classifier gave the best result among the algorithms used. Moreover, we also tried to optimize our proposed model and made a comparison among state-of-the-art approaches proposed by different researchers, on the same dataset.

International journal of computers, 2020

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.

Indian Institute of Education, 2023

Machine learning has the concept of experiential learning which recognize patterns and make accurate predictions of future events. It has been also utilized in various sectors including the health sector. Machine learning is used to find the abnormalities at an early stage of various type of disease. The various Machine Learning algorithms have been given different types of accuracy for the model created for the same data set. For the present study the researcher has used the secondary data of breast cancer patients. The exploratory analysis of this dataset has been done and further the MLmodel has been created by the Researcher.In this research, the comparative analysis of different ML algorithms has been done, for this the machine has been trained and tested by three ML algorithms such as Logistic Regression, Support Vector Machine and K-Nearest Neighbour Algorithm. The accuracy of the each model has compared. The confusion matrix in this research has been used to check the accuracy of the model, and found Support Vector Machine (SVM) is the best with higher accuracy for the current data set. For the present study the Python Language and its various libraries has been used to create a model.