According to estimates provided by the World Health Organization, the cost of medical care is going up all over the world. Because of this, health care systems need to improve their capacity to treat patients in a timely and cost-effective manner in order to slow the rate at which costs are increasing. Nowadays, motivators are all the rage. I will argue that the use of machine learning techniques to healthcare will improve treatment outcomes, and I will be in a position to construct health care information systems. The results of my research highlight creative new methods for promoting collaboration. Skills in machine learning and in working with medical professionals, in addition to cutting-edge methods for simulating the responses of patients to therapy. Adapting is a necessary aspect of my collaboration with other therapeutic providers. Biopsies taken for breast cancer that turn out to be benign are being used to address the challenging challenge of identification using machine learning algorithms. I began by becoming a student of inductive logic programming in order to choose rules that do erroneously identify cancer cases and display encouraging outcomes that do not serve to in service of the clinical goal and shine light on the work that needs to be done. I then provide a platform for communication and collaboration between AI researchers and medical practitioners. By drawing on the experience of doctors, a model may be developed and modified in such a way that the objective of conservatism is to miss no cases of malignancy. The things that I've discovered through the course of my work include calculating individual responses to treatment in the marketing discipline, which is where uplift modelling is being utilized to perform the two most important roles in the field of medicine. Priority should be given to identifying those patients within a patient population who are most likely to suffer a heart attack. Because of COX-2 inhibitor medication, and the second reason is learning about the distinctive characteristics of in situ breast cancer that affects older women. I will begin by presenting a statistical learner that constructs Bayesian networks with the goal of minimizing the arithmetic mean squared error (RMSE). I will then show that trained networks are capable of capturing the clinically significant characteristics of breast cancer that is relatively slow-growing and in situ. After that, I will present a support vector machine that I developed expressly for the purpose of boosting AUU. I will also display some encouraging results for COX-2 drugs and breast cancer duties, in addition to a synthetic version of a marketing assignment.

Machine Learning , Artificial Neural Network