I have given a coding presentation (Python language) on how to prepare data for predictive modeling, check it out after you learn the theory behind predictive modeling in healthcare. We will start by knowing why predictive analytics? And also understand how predictive models work. Before we continue, imagine how change can happen in this world, when you actually only get drugs for those diseases you are suffering from right now? And how wonderful would it be to receive information only on relevant healthcare products? And most importantly, ask yourself what quality of life humanity would achieve just by predicting the most dangerous diseases simply by looking at patterns within medical records, current symptoms, and historical health data. Okay, all these doctors can do, but how efficient do you think they could be? Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essayDaniel Faggella (2018) Machine learning in healthcare enables the extraction of high-quality data that can be deep and more accurate, through the use of computers that can learn based on experience, thus bringing the potential uses of data in healthcare at a higher and truly new level. An algorithm's ability to recognize patterns that even the best doctors in the world would not easily notice, extracting previously unrevealed correlations that in turn improve the entire medical and surgical practice. Algorithms can identify correlations between different sutures used on specific patient injuries and also the likelihood of an infection. These pattern recognitions communicate potential medical and health problems at an individual level among patients with reference to before the problem occurs and manifests. Simple definition: Predictive modeling is a strategy that uses mathematical and computational methods to predict an opportunity or outcome. A mathematical approach uses a condition-based model that describes the phenomenon of underthinking. The model is used to calculate a result in a future state or time in view of changes to the model's data sources. Model parameters help clarify how model inputs affect the outcome. Use cases for predictive healthcare analytics Predict chronic disease and maintain population health Using predictive modeling to proactively identify patients who are at highest risk for poor health outcomes and who will benefit most from the intervention is a solution believed to improve risk management for suppliers moving to value-based analytics. Learn more about a 9-layer deep convolutional neural network (CNN) developed to monitor heart activity. Health systems and hospitals incur high costs and insufficient resources due to unplanned patient returns. By improving care transitions and implementing care coordination strategies, predictive analytics provides healthcare providers with an alert about an event where a patient's risk factors indicate a high probability of readmission for a particular patient within a window of 30 days. By predicting patient traits that may have a high impact on the likelihood of readmission, these can be quickly identified and can provide healthcare providers with further guidance especially on when to focus resources on follow-up and how to design discharge planning protocols to prevent rapid returns to hospital.Prevent suicide e.