Green Simple Healthcare Diagnosis Support Infographic Presentation (1).pdf

Healthcare Diagnosis PPT template

1. HEALTHCARE DIAGNOSIS SUPPORT
Assisting doctors in early detection and accurate diagnosis
Collect Medical
RecordsUse data from patient
histories, lab tests, and
imaging.
Train Classification
ModelTeach the system to
detect conditions based
on symptoms.
Recommend Next
TestsSuggest further
diagnostics based on
model confidence.
Preprocess for
PrivacyAnonymize and
standardize sensitive
health data.
Highlight Risk
IndicatorsShow probabilities and
highlight red flags.
Support Clinical
DecisionsPresent results to aid—
not replace—medical
judgment.

2. CREDIT SCORING WORKFLOW Predicting borrower reliability to reduce default risks Ingest Credit
History Clean and
Normalize Data Include loan history,
payments, and
financial behavior. Standardize values for
fair evaluation. Train Scoring
Model Estimate
creditworthiness
using labeled data. Classify Risk
Levels Assign scores from
low to high risk. Automate Loan
Decisions Recommend
approvals, rejections,
or manual review. Track Model
Fairness Monitor for bias and
adjust rules if needed.

3. SMART TRAFFIC MANAGEMENT
Improving flow and reducing congestion in urban areas
Collect Real-Time
Vehicle Data
Detect Congestion
Zones
Adjust Signal
Timings
Reduce Overall
Delay
Predict Traffic
Build-Up
Inform Navigation
SystemsUse sensors, GPS, and
traffic cameras. Identify hotspots based
on density patterns. Dynamically update lights
based on prediction. Evaluate impact through
traffic KPIs. Forecast flow disruptions
before they occur. Share routes with apps or
vehicle systems.

4. PERSONALIZED EDUCATION PATHS
Adapting learning experiences to student needs
Analyze Student
Progress
Adjust Teaching Pace
Detect Learning Gaps
Track Skill AcquisitionMonitor scores, engagement,
and comprehension data. Adapt schedule and difficulty
based on mastery. Identify weak areas needing
attention. Measure improvement over
time per subject.
Recommend Tailored
Content
Inform Teacher
DecisionsSuggest lessons based on
learning profile. Provide insights for
personalized teaching
strategies.

5. E-COMMERCE PRODUCT RECOMMENDATION
Helping customers find what they want faster
Track Browsing and
Purchase Behavior
Generate Real-Time
Suggestions
Build User Profiles
A/B Test
RecommendationsMonitor clicks, views, and
transactions. Display items likely to interest
the user. Understand preferences and
buying intent. Compare layouts to see what
increases conversion.
Cluster Similar
Products
Improve Based on
FeedbackGroup items by use, look, or
function. Learn from ignored or
purchased items.

6. FRAUD DETECTION IN FINANCE
Protecting transactions with real-time anomaly detection
Collect Transaction
Data
Send Alerts
Automatically
Detect Unusual
Patterns
Block Suspicious
ActivityLog amounts, times, locations,
and devices used. Notify the user or security team
when needed. Flag behaviors that deviate
from norms. Measure improvement over
time per subject.
Score Transactions
Review and Refine
ModelsRate each action by likelihood
of being fraudulent. Update rules with confirmed
fraud cases.

7. INVENTORY MANAGEMENT IN RETAIL
Optimizing stock levels based on predictive insights
Analyze Past Sales
Data
Set Dynamic Stock
Limits
Forecast Item Demand
Avoid Over-StockingDetect trends by season,
category, and location. Adjust minimum levels by
predicted movement. Predict future sales to prevent
stockouts. Flag items at risk of excess
inventory.
Recommend Restock
Timing
Reduce Holding CostsSuggest optimal reorder dates
per product. Maintain efficient inventory
flow over time.

8. Schedule Maintenance
Proactively
PREDICTIVE MAINTENANCE IN MANUFACTURING
Preventing equipment failure through early intervention
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Monitor Equipment
Sensors
Predict Failure
Timelines
Detect Deviation
PatternsTrack temperature, pressure, and
vibration levels. Estimate when a part might
malfunction. Identify early signs of abnormal
behavior. Avoid downtime by servicing
before failure.
Log Maintenance
Outcomes
Extend Equipment
LifespanLearn from repair history to
improve models. Reduce waste and improve
operational efficiency.

9. REAL ESTATE PRICE ESTIMATION
Assisting buyers, sellers, and agents with fair pricing
Gather Local
Market Data
Clean and
Standardize
InputsInclude home
features, location, and
sale history. Format values
consistently across
listings.
Train
Regression
ModelPredict property value
based on features.
Generate Price
SuggestionsProvide
recommended price
ranges.
Compare with
Market ListingsDetect overpricing or
undervaluation.
Adjust with
Feedback LoopsRefine model using
real sale outcomes.
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10. Identify Trends Over
Time
PUBLIC SENTIMENT ANALYSIS WORKFLOW
Understanding how people feel on social platforms
Ingest Social Media
Content
Classify Sentiment
Clean and Tokenize
TextPull text from posts, comments,
and hashtags. Label as positive, neutral, or
negative. Remove noise and prepare
text for analysis. Track shifts in opinion about
topics or brands.
Visualize Geo
Sentiment
Support Public
Messaging DecisionsMap opinions by location if
possible. Use insights to adjust
communication strategies.
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