Breast Cancer Likelihood Prediction Using Bayesian Networks | Chapter 5 | Current Innovations in Medicine and Medical Science Vol. 4

 Worldwide, breast cancer is the most prevalent type of cancer among women. With the help of medical professionals and machine learning algorithms, early detection of breast cancer can result in earlier diagnosis, treatment, patient prolongation, lower treatment costs, and fewer fatalities. To accomplish this, we investigated various methods to improve the accuracy for predicting the likelihood and early diagnosis of breast cancer in this study. Bayesian Network machine learning models were examined to assess the vast medical data. We employed the Breast Cancer Surveillance Consortium (BCSC) dataset comprising 53,370 screening records, the machine learning package, and WEKA (Waikato Environment for Knowledge Analysis). The variable "breast cancer history" was the primary class variable to be predicted in this dataset of thirteen variables, with "class 0" denoting a negative breast cancer diagnosis and "class 1" denoting a positive breast cancer diagnostic. Three separate Bayesian networks—K2 (Hill Climbing Algorithm), TAN (Tree Augmented Naive Bayes), and Simulated Annealing—were produced by various structure-determination algorithms. Using the performance metrics Recall and Precision, we compared the Bayes Networks' performances and related network structures. Our findings show that TAN, K2, and the Bayesian Network produced by Simulated Annealing (SA) had the best overall prediction performances. For Class 1, K2 had the highest Recall, followed by SA and TAN, and TAN had the highest Precision. K2 had the lowest Recall and highest Precision. When the network architectures of the three Bayesian Networks were compared, it became clear that the SA had the highest number of connections between variable nodes and the most complicated network structure, followed by TAN and K2.

Author(s) Details:

G. Nanda,
School of Engineering Technology, Purdue University, West Lafayette, IN-47907, USA.

R. Sundararajan,
School of Engineering Technology, Purdue University, West Lafayette, IN-47907, USA.

Please see the link here: https://stm.bookpi.org/CIMMS-V4/article/view/8394

Reducing Threats by Using Bayesian Networks to Prioritize and Combine Defense in Depth Security Measures| Chapter 6 | New Ideas Concerning Science and Technology Vol. 1

 This paper analysed whether the Bayesian Network Model (BNM) can be effectively used to prioritise in-depth security tools and procedures for protection and to combine those steps to reduce cyber threats. A balance between the defence capability and cost , efficiency, and operational considerations is recommended by the strategy. The methods used in this study were to search 24 peer-reviewed cybersecurity papers from leading cybersecurity journals using the Likert Scale Model for the article's list of in-depth safety measures (tools and procedures) and the risks to be mitigated by those measures. In order to see if the Likert scale and the Bayesian Network Model could be effectively implemented to prioritise and combine steps to minimise cyber attacks against organisational and private computing networks, the security tools and procedures were then compared. The study results refute the H0 null hypothesis that the relationship between the prioritisation and the combination of 24 Cybersecurity Article safety instruments and procedures (independent variables) and cyber threats (dependent variables) is not influenced by BNM.

Author(s) Details

Dr. Rodney Alexander
Hutchinson Community College, Hutchinson, Kansas, USA.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/308