Predictive Analysis for Decision Making Using Machine Learning in Health Care - Prasadu, Walli; Kumar, Ravishankar; Kiran Katta, Ravi; Sagar, Pinki (International Journal of Computer Sciences and Engineering, 2023)
Breast Cancer has turned into the normal reason for death among ladies. There are several machine learning algorithms that can be used for breast cancer predictive analysis, such as logistic regression, decision trees, random forests, support vector machines, and neural networks. These algorithms can be trained on large datasets of patient information, including demographic data, medical history, and genetic markers, to identify patterns and make accurate predictions. One of the key benefits of machine learning in breast cancer predictive analysis is the ability to personalize treatment plans based on individual patient characteristics. By analyzing a patient`s unique combination of risk factors, doctors can develop tailored treatment plans that are more effective and less invasive. Our point is to group whether the breast cancer is harmless or dangerous and foresee the repeat and non-repeat of threatening cases after a specific period. To accomplish this we have utilized AI strategies, for example, “Support Vector Machine”, “Logistic Regression”, “KNN and Naive Bayes”. We additionally have investigated the precision of expectation of by applying different calculation on the new arrangement of information that has been joined. This paper explores the use of predictive analytics in healthcare decision-making through machine learning. The application of machine learning algorithms in healthcare can assist in identifying patterns and trends in patient data, which can then be used to predict potential health risks, recommend treatment options, and optimize healthcare delivery. The paper discusses various predictive analytics techniques such as decision trees, random forests, logistic regression, and neural networks, and their applications in healthcare. Additionally, the paper also highlights the challenges associated with the implementation of predictive analytics in healthcare, such as data quality, privacy concerns, and ethical issues. Finally, the paper concludes by emphasizing the need for healthcare organizations to leverage predictive analytics to improve patient outcomes, reduce costs, and enhance the quality of care.