OncoView UAE is an interactive Tableau dashboard built using a synthetic cancer dataset representing fictional cases across the United Arab Emirates (UAE). The data is sourced from Kaggle and licensed under the MIT License.

Cancer is a sensitive public health issue affecting a vulnerable population. This dashboard does not make any medical interpretations or provide clinical insights. Instead, it serves as a technical showcase of how cancer-related data can be visualized to explore patterns in demographics, diagnosis, treatment timelines, and patient outcomes.

• Tableau Public – For building multi-layered dashboards
• Kaggle Dataset – Synthetic cancer dataset (fictionalized and anonymized) from MIT
• Calculated Fields & Filters – For interactive exploration
• Story Navigation – Button-based section navigation within dashboard
• Charts – Line plots, bar charts, maps, boxplots, donut charts, bubble charts

• Demonstrate data storytelling through layered Tableau dashboards
• Explore how a synthetic cancer dataset can be used to simulate real-world scenarios
• Visualize important variables like age, gender, cancer stage, comorbidities, and treatment timelines
• Present a non-clinical, safe, and ethical representation of sensitive healthcare topics

1. Cancer Demographics
   Age and gender breakdowns, common cancer types, and nationality analysis

2. Geographic and Temporal Trends
   Trends by Emirate and diagnosis year with choropleth map visualizations

3. Clinical Characteristics
   Treatment types by cancer stage, comorbidities, and stage-at-diagnosis distributions

4. Treatment Timeliness and Survival Analysis
   Time from diagnosis to treatment and survival durations by cancer type

5. Patient Status
   Outcomes across hospitals and most common causes of death

6. Risk Factors and Lifestyle
   Smoking status analysis and impact on cancer diagnosis/outcomes

7. Body Metrics and Prognosis
   BMI and weight distribution of patients segmented by treatment outcomes

1. Healthcare Dashboards – Demonstrates ability to design responsive, filter-driven dashboards for complex clinical data.

2. Analytical Thinking – Showcases skill in identifying key variables that affect patient outcomes, without breaching ethical limits.

3. Data Literacy & Storytelling – Converts raw data into insights using charts and intuitive navigation for different user personas.

4. Public Health Readiness – Provides a sample framework that could be extended for real clinical data systems (with appropriate privacy controls).

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MIT