Data Mining in Healthcare using Machine Learning Techniques

Classification techniques have become increasingly important in healthcare due to the need for accurate and efficient disease diagnosis, treatment planning, and patient care. Supervised learning algorithms, such as decision trees, logistic regression, and support vector machines, are used for disease diagnosis, predicting patient outcomes, and identifying potential risk factors. Classification techniques are also used in image recognition and analysis, such as in radiology and pathology. Classification is a supervised learning technique used to predict the class or category of an instance based on the given set of attributes. This research study explores the use of classification techniques in data mining for healthcare applications. The goal of this study is to apply classification algorithms such as Naive Bayes, Logistic Regression and Random Forest to healthcare datasets and evaluate their performance. The datasets used in this study include patient information such as demographics, medical history, and diagnosis. The findings suggest that the classification techniques can be effective in data mining for healthcare applications, enabling healthcare professionals to make more informed decisions based on patient data.