How does the use of machine learning algorithms affect the accuracy of GSD predictions in various industries?
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Machine learning algorithms play a crucial role in predicting various health conditions, including heart disease and chronic kidney disease, leading to improved accuracy in disease predictions. Studies show that algorithms like logistic regression, K-nearest neighbor, random forest, and decision trees enhance prediction accuracy in heart disease detection . Similarly, in chronic kidney disease prediction, combining preprocessing techniques and collaborative filtering with multiple ML algorithms results in more accurate predictions . Moreover, in the context of employee attrition, logistic regression, K-nearest neighbors, support vector machine, random forest, and decision trees are utilized to forecast employee turnover, with logistic regression achieving an accuracy of 88.0952% . Therefore, the application of machine learning algorithms significantly improves prediction accuracy across various industries, aiding in early detection and proactive decision-making.
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