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1. TITLE SLIDE SEASONAL DISEASE OUTBREAK MONITORING AND PREDICTION SYSTEM USING BIG DATA ANALYTICS B.Tech CSE (Data Science) – S5 Big Data Analytics – Minor Project Team Members…
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1. TITLE SLIDE SEASONAL DISEASE OUTBREAK MONITORING AND PREDICTION SYSTEM USING BIG DATA ANALYTICS B.Tech CSE (Data Science) – S5 Big Data Analytics – Minor Project Team Members Dheeraj Prasad – Register No: __________ Fathima Hena – Register No: __________ Fathima Zamreen – Register No: __________ Mefin KP – Register No: __________ 2. CONTENT PAGE Problem Description Proposed Solution System Architecture Data Flow Diagram Flow Chart Conclusion 3. PROBLEM DESCRIPTION Existing Problem Seasonal diseases such as dengue, malaria, influenza and viral fever tend to increase during particular seasons. Outbreaks may not be detected early using traditional monitoring methods. Large amounts of disease-related data are generated by hospitals and healthcare centers. Manually analyzing large datasets is time-consuming and difficult. Environmental factors such as rainfall, temperature and humidity can influence disease spread. It is difficult to identify high-risk regions before a major outbreak occurs. Healthcare authorities may not have enough time to prepare medicines, hospital beds and medical staff. Therefore, an efficient system is required for early monitoring and prediction of seasonal disease outbreaks. Proposed Solution Collect historical and current disease and environmental data. Use Big Data Analytics to process and analyze large datasets. Identify seasonal disease patterns and trends. Use Machine Learning to predict possible outbreaks. Identify regions with higher outbreak risk. Provide a dashboard with graphs, trends, risk levels and predictions. Generate early alerts to support preventive actions. Help healthcare authorities plan resources before an outbreak becomes severe. 4. DESIGN A. System Architecture You can show the architecture in 5 main stages: 1. Data Collection Data is collected from: Hospitals and healthcare centers Disease surveillance records Weather/environmental sources Public health databases Historical disease datasets ⬇️ 2. Data Preprocessing Remove duplicate and incorrect data Handle missing values Clean and standardize the data Combine disease and weather information ⬇️ 3. Big Data Processing & Analytics Store large datasets Analyze disease trends Identify seasonal patterns Find relationships between weather and disease occurrence ⬇️ 4. Machine Learning Prediction Extract important features Train the prediction model using historical data Predict outbreak probability Classify risk as Low / Medium / High ⬇️ 5. Dashboard & Alert System Display disease trends Show predicted risk Display high-risk regions Generate early warnings Support healthcare decision-making Architecture Flow Data Sources → Data Collection → Preprocessing → Big Data Analytics → ML Prediction → Dashboard & Alerts B. Data Flow Diagram (DFD) Input Hospitals / Health Centers ⬇️ Disease Data Weather / Environmental Sources ⬇️ Temperature, Rainfall, Humidity ⬇️ Disease Outbreak Monitoring & Prediction System Inside the system: Data Collection → Data Processing → Data Analysis → Prediction ⬇️ Output Disease outbreak prediction Risk level High-risk locations Disease trend graphs Early warning alerts ⬇️ Healthcare Authorities Simple DFD representation: Hospitals / Health Centers ──┐ │ Weather / Environmental ─────┤ ↓ ┌───────────────────┐ │ Disease Outbreak │ │ Monitoring & │ │ Prediction System │ └───────────────────┘ │ ┌──────────────┼──────────────┐ ↓ ↓ ↓ Predictions Risk Areas Alerts │ │ │ └──────────────┼──────────────┘ ↓ Healthcare Authorities C. Flow Chart Use this flow in your PPT: START ↓ Collect Disease Data ↓ Collect Weather Data ↓ Data Preprocessing ↓ Store & Process Data ↓ Analyze Seasonal Patterns ↓ Apply ML Prediction ↓ Predict Outbreak Risk ↓ Display Results ↓ Generate Early Alert ↓ END 5. CONCLUSION The proposed system provides an efficient approach for monitoring seasonal diseases. Big Data Analytics helps process and analyze large volumes of healthcare and environmental data. Machine Learning can identify seasonal patterns and predict possible outbreaks. Weather factors can be analyzed along with historical disease data to improve prediction. The system can identify high-risk regions and disease trends. Early alerts can help healthcare authorities take preventive measures. The system can also support better planning of medicines, hospital beds and healthcare staff. Overall, the proposed system can contribute to early detection, better preparedness and effective management of seasonal disease outbreaks. 6. THANK YOU THANK YOU Follow Design: {"palette":["Chalk white #F9FBFB — primary clean background","Graphite black #111315 — text and primary UI framing","Electric cobalt #2563EB — big data pipeline nodes and processing stages","Outbreak orange #EA580C — early warning thresholds and anomaly detection","Vapor gray #EEF2F6 — pipeline cards and flow background blocks","Steel divider #D1D5DB — flow chart arrows and architecture borders"],"fonts":{"Outfit":"https://fonts.googleapis.com/css2?family=Outfit:wght@100..900&display=swap","Manrope":"https://fonts.googleapis.com/css2?family=Manrope:wght@200..800&display=swap","Chivo Mono":"https://fonts.googleapis.com/css2?family=Chivo+Mono:ital,wght@0,100..900;1,100..900&display=swap"},"type":"Outfit in bold for high-impact section headlines; Manrope in regular and medium weights for clear problem descriptions and analytical takeaways; Chivo Mono for architectural phase counters (STAGE_01, DFD_INPUT, MODEL_ML) and register number metadata.","layout":"Engineered technical block layout with a rigid 4-column rhythm; 40px gutters; left-aligned anchor column for key problem/stage statements paired with structured right-hand process cards; linear flowchart oriented in a clean single-track progression.","framework_treatment":"Light gray architectural bounding boxes with sharp 4px corners; high-visibility electric cobalt accent bars marking active data processing stages; mono-spaced metadata badges for team details and system inputs; clean vector-like workflow paths using thin solid lines with directional pointer heads.","feels_like":"An Apache Spark and Databricks computational health engineering symposium deck"}
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This community presentation was shared by Fathima Zamreen as a complete 11-slide example. 1. TITLE SLIDE SEASONAL DISEASE OUTBREAK MONITORING AND PREDICTION SYSTEM USING BIG DATA ANALYTICS B.Tech CSE (Data Science) – S5 Big Data Analytics – Minor Project Team Members… The preview lets you review the full sequence in order, rather than judging the design from a single cover image.
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