Evaluation of Machine Learning techniques in disease diagnosis and prediction


A branch of artificial intelligence (AI) called “machine learning” focuses on creating models and algorithms that let computers learn from data and make judgments without needing to be explicitly programmed for every assignment. All it does is provide systems with the capacity to automatically learn from and get better with experience.

There are several types of machine learning techniques, including:
1.Supervised Learning.
2.Unsupervised Learning.
3.Semi-Supervised Learning.
4.Reinforcement Learning.

The healthcare industry increasingly relies on data-driven approaches to enhance diagnostic accuracy and patient care. The successful development and evaluation of machine learning models for disease diagnosis and prediction offer promising implications for clinical practice and public health. By leveraging these models, healthcare professionals can augment their diagnostic capabilities, facilitate early disease detection, and tailor interventions for improved patient outcomes.