Predictive Analytics: Case Studies on Predictive Analytics for Healthcare
Predictive Analytics | Beginner
- 11 videos | 1h 27m 23s
- Includes Assessment
- Earns a Badge
Healthcare aims to improve the health of individuals, but generally, healthcare systems tend to be extremely strained. Using artificial intelligence (AI) with healthcare could potentially mitigate this strain on the system. In this course, examine how AI is used in healthcare, how to evaluate classification models, and the metrics that are significant in models used in disease diagnosis. Next, discover the importance of a model's recall or sensitivity and the computation of the ROC curve and AUC metrics. Finally, explore the process of compiling the datasets, training, and evaluating models from research papers that take a general look at the application of AI in disease and specific ailment diagnosis. Upon completion, you'll be able to identify healthcare use cases for AI and its limitations.
WHAT YOU WILL LEARN
Discover the key concepts covered in this courseRecall how ai can help improve outcomes in the healthcare sectorRecognize the significance of classification metrics such as accuracy, precision, and recallIllustrate how the roc curve and auc metrics are computedOutline the setup of a meta-analysis study on ai for healthcareIdentify what ml models diagnosing disease could potentially accept as input
List the different types of ml models used for diagnosing diseasesOutline the setup of a study on detecting heart disease using aiRecall the performance of various models used to diagnose heart diseaseOutline the setup of a study that researched the application of ml to diagnose chronic kidney diseaseSummarize the key concepts covered in this course
IN THIS COURSE
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