Which MLlib Algorithms Are Useful for Cancer Research?
Several algorithms provided by MLlib can be particularly beneficial in cancer research:
1. Classification Algorithms: Algorithms like Logistic Regression, Decision Trees, and Random Forests can classify different types of cancer or predict the presence of cancer based on patient data. 2. Clustering Algorithms: K-means and Gaussian Mixture Models can help in identifying different subtypes of cancer by clustering similar patient profiles or genomic data. 3. Collaborative Filtering: Although traditionally used for recommendation systems, collaborative filtering can be used to predict treatment outcomes based on similar patient profiles. 4. Principal Component Analysis (PCA): PCA can reduce the dimensionality of genomic data, making it easier to identify significant genetic markers. 5. Survival Analysis: Though not directly available in MLlib, survival analysis can be integrated to predict patient survival rates based on different treatment plans.
