Application of Machine Learning Algorithm in Medical Diagnosis

Abstract

The development of machine learning algorithms has 
revolutionized the medical data categorization industry through 
the introduction of artificial intelligence. The development of 
mathematical models employing statistical theory to draw 
conclusions from samples has proven to be a fruitful application 
of machine learning. Due to the enormous amounts of patient 
data, machine learning algorithms have been implemented into 
the medical industry to make crucial diagnostic decisions. By 
analyzing medical imaging data and learning from labelled 
examples, machine learning algorithms have demonstrated 
tremendous potential for automatically classifying and identifying 
diseases. Using the Heart Disease UCI dataset as an example, 
this study evaluates the accuracy of various cardiovascular 
disease prediction algorithms using accuracy ratings and 
confusion matrices. It insinuates that machine learning algorithms, 
such as logistic regression, random forest, deep neural networks, 
and gradient boosting, have the potential to improve healthcare 
decision-making processes and demonstrates the importance of 
machine learning algorithms in this field. The development of 
machine learning algorithms has revolutionized the medical data 
categorization industry through the introduction of artificial 
intelligence. The development of mathematical models employing 
statistical theory to draw conclusions from samples has proven to 
be a fruitful application of machine learning. This study evaluates 
the performance of four prominent machine learning algorithms in 
various medical contexts, including cardiac care, trauma units, 
breast cancer diagnosis, etc.