Generative AI Hackathon with IBM Granite

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MASTERMIND

Mohanakrishna

Chaitanya_13

adityamurthi

**Cancer Prediction Using Machine Learning**

### **Introduction**
Cancer is a major global health challenge, and early detection is key to improving survival rates. Machine learning (ML) enhances oncology by analyzing medical data for early diagnosis, classification, and personalized treatment.

### **1. Data and Preprocessing**
ML utilizes data from:
- **Electronic Health Records (EHRs)**
- **Genomic Data**
- **Medical Imaging**
- **Biomarkers and Lab Tests**

Data preprocessing includes cleaning, normalization, and feature selection to improve accuracy.

### **2. ML Algorithms for Cancer Prediction**
- **Supervised Learning:** Logistic Regression, Decision Trees, SVM, Neural Networks.
- **Deep Learning:** CNNs for imaging, RNNs for time-series data, Autoencoders for genomic analysis.

### **3. Applications**
- **Breast Cancer:** Detects tumors in mammograms.
- **Lung Cancer:** Identifies malignant nodules in CT scans.
- **Skin Cancer:** Classifies skin lesions.
- **Prostate Cancer & Leukemia:** AI enhances early detection.

### **4. Benefits**
- **Early Detection & Accuracy**
- **Non-Invasive Diagnosis**
- **Personalized Treatment**
- **Faster Decision-Making**

### **5. Challenges & Future Directions**
- **Data Privacy & Bias Issues**
- **Explainable AI for Trust**
- **Integration into Healthcare Systems**
- **Advances in AI for Drug Discovery**

### **Conclusion**
Machine learning is revolutionizing cancer prediction through early detection and personalized treatment. Continued advancements will further improve patient outcomes and cancer care.



Machine learning predicts cancer by analyzing medical data, aiding early detection, diagnosis, and personalized treatment for better patient outcomes.

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