Lung Cancer Dataset

Key Features of the Dataset

The Lung Cancer Dataset contains valuable, structured data that can be used to build highly accurate cancer detection models. It includes a wide variety of symptoms and health factors related to lung cancer, such as:

• Patient Demographics: Age, gender, and smoking history to assess the risk factors associated with lung cancer.
• Symptoms Data: Detailed information on common lung cancer symptoms like fatigue, shortness of breath, coughing, chest pain, and more.
• Medical History: Comorbidities like chronic diseases and prior health conditions that may impact diagnosis and treatment plans.

Dataset Categories:

1. Patient Demographics
   • Age: Age at diagnosis, aiding in studies focused on age-related trends in lung cancer.
   • Gender: Data on patient gender for gender-specific analysis.
   • Smoking Status: Categorized as current smoker, former smoker, or non-smoker, allowing for insights into smoking-related lung cancer risks.
2. Medical History
   • Comorbidities: Chronic conditions like COPD that may influence lung cancer progression and treatment.
3. Clinical Symptoms
   • Fatigue, Anxiety, and Coughing: Key symptoms that help model lung cancer detection.
   • Shortness of Breath, Swallowing Difficulty, and Chest Pain: Crucial symptoms for lung cancer detection models.
4. Clinical Data
   • Vital Signs: Records of blood pressure, heart rate, respiratory rate, and more to monitor patient condition and progress.

Scientific Value: This dataset provides an invaluable resource for researchers, data scientists, and health tech companies working on AI-powered healthcare solutions. By leveraging the data within this dataset, developers can create predictive models for early lung cancer detection, improving treatment outcomes and saving lives.