Mohana krishna Vedala@Mohanakrishna

**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.

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