The diagnosis and treatment of patients in the healthcare industry are greatly aided by data analytics. Massive amounts of data should be handled using machine learning approaches to provide tools for prediction and categorization to support practitioner decision-making. Based on the kind of tumor, disorders like breast cancer can be categorized. The difficulties associated with evaluating vast amounts of data should be overcome by discovering an efficient method for categorization. Based on the Bayesian method, we analyzed the influence of clinic pathological indicators on the prognosis and survival rate of breast cancer patients and compared the local resection value directly using the lymph node ratio (LNR) and the overall value using the LNR differences in effect between estimates. Logistic regression was used to estimate the overall LNR of patients. After that, a probabilistic Bayesian classifier-based dynamic regression model for prognosis analysis is built to capture the dynamic effect of multiple clinic pathological markers on patient prognosis. The dynamic regression model employing the total estimated value of LNR had the best fitting impact on the data, according to the simulation findings. In comparison to other models, this model has the greatest overall survival forecast accuracy. These prognostic techniques shed light on the nodal survival and status particular to the patient. Additionally, the framework is flexible and may be used with various cancer types and datasets.

Background: The existence of aberrant myocardial activity and function in the exclusion of those other cardiovascular events, such as atherosclerosis, hypertension, and severe valve disease, is known as diabetic cardiomyopathy. Diabetes patients are much more prone to death from cardiovascular illnesses than from any other cause, and they also have a 2–5 fold higher likelihood of acquiring cardiac failure and other complications. Objective: In this review, the pathophysiology of diabetic cardiomyopathy is discussed, with an emphasis on the molecular and cellular irregularities that arise as the condition progresses, as well as existing and prospective future treatments. Method: The literature for this topic was researched utilizing Google Scholar as a search engine. Before compiling the review article, several research and review publications from various publishers, including Bentham Science, Nature, Frontiers, and Elsevier, were investigated. Result: The abnormal cardiac remodelling, marked by left ventricular concentric thickening and interstitial fibrosis contributing to diastolic impairment, is mediated by hyperglycemia, and insulin sensitivity. The pathophysiology of diabetic cardiomyopathy has been linked to altered biochemical parameters, decreased calcium regulation and energy production, enhanced oxidative damage and inflammation, and a build-up of advanced glycation end products. Conclusion: Antihyperglycemic medications are essential for managing diabetes because they successfully lower microvascular problems. GLP-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors have now been proven to benefit heart health by having a direct impact on the cardiomyocyte. To cure and avoid diabetic cardiomyopathy new medicines are being researched, including miRNA and stem cell therapies.