Lung cancer prediction using clinical data

1. Preemptive Diagnosis of Lung
Cancer Using Clinical Data in
Machine Learning

2. Abstract
• Aim: To design and implement an ML model
for lung cancer detection and diagnosis.
• Dataset: Clinical data with variables like Name,
Age, Gender, Medical History.
• Preprocessing: Various ML algorithms used to
create a predictive model.
• ML Packages: NumPy, Matplotlib, Pandas.
• Scope: Extendable to complex data and
advanced medical imaging.

3. Work Progress
• Objective: Develop a machine learning model
for early lung cancer detection.
• Software & Tools: HTML, CSS, Python, Jupyter,
SQL, Flask, Power BI.
• Learned Python & essential data structures for
project execution.

4. Action Plan for February
• • Gaining familiarity with ML concepts for
medical data analysis.
• • Learning ML algorithms essential for model
training and evaluation.

5. Conclusion
• • The project aims to enhance early lung
cancer detection with ML.
• • Next Steps: Implementing ML models,
testing performance, and deployment.