Machine Learning Models Used in Predicting Various Diseases

For a disease prediction scenario, we have to use different types of machine learning models based on the problem. Here are some of the models that are widely used.



  • Logistic Regression: This is used for solving the most common binary classification problem of logistic regression. It is especially good at finding out which specific patient will have a disease (such as cancer or diabetes) when presented with the symptoms. 
  • Random Forest: It is an ensemble method, which means it constructs a multitude of decision trees and output the mode class or mean prediction. It is useful with numerous data dimensions and disease-variate interactions. 
  • Support Vector Machines (SVM): SVM is a powerful algorithm in case data is not linearly separable. This is utilized for complex diseases to classify like cancers, which permanently finds the hyperplane to classify classes. 
  • Neural Networks: Powered by deep learning methods employing neuronal networks, particularly in scenarios where the task is complex, data is scattered and primarily unstructured containing image data in clinical records. 
  • K-Nearest Neighbors (KNN): This is a very basic algorithm but it uses closeness between data points with known variables. It is mainly applied to disease prediction if the patients are similar.

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