Ge Healthcare
Data Mining for Evaluating Business Performance
Visual Elements
Cost Analysis
Data Mining
Identify Industry Opportunities
Criteria
Understanding Predictive Analytics
Performance Metrics 
Manufacturing Overall Equipment Effectiveness (OEE), First Pass Yield
Healthcare Patient Satisfaction Score, Average Length of Stay Technology Monthly Recurring Revenue (MRR), Customer Churn Rate
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Data Interpretation Techniques 
Healthcare Analytics: Improving patient outcomes and operational efficiency
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Data Mining for Evaluating Business Performance 
Manufacturing Quality control Reduction in defects and waste
Healthcare Patient outcome prediction Improved treatment plans Telecommunications Churn prediction Retention strategies Challenges
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Visual Elements 
Healthcare Analytics: Visual elements are employed to monitor patient outcomes and operational efficiencies
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Cost Analysis 
Healthcare: Evaluating the costs of treatments and procedures to improve financial performance
...Automated Reporting: Machine learning can automate the
generation of cost reports, reducing the time and effort required for manual analysis
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Data Mining 
Healthcare: Patient diagnosis, treatment effectiveness, and drug discovery
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Identify Industry Opportunities 
Technology Emerging AI Solutions Increased market share by 25% Company B
Healthcare Telehealth Services Expanded customer base by 40% Company C Retail Online Shopping Trends Boosted
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Criteria 
This is crucial in industries such as finance,
healthcare, and data protection
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Understanding Predictive Analytics 
Applications of Predictive Analytics Predictive analytics is applied across various sectors, including:
Healthcare: Predicting patient outcomes and optimizing treatment plans
...Real-Time Analytics: The ability to analyze data as it is
generated for immediate insights
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Concepts 
Industry Application Finance Fraud detection and risk assessment
Healthcare Predictive analytics for patient outcomes Retail Customer segmentation and inventory management Manufacturing
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