Use of Machine Learning Techniques in Healthcare: A Brief Review of Cardiovascular Disease Classification

Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS’s performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds.

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Applications of Machine Learning Techniques in Disease Classification From Medical Images

Advances in Medical Technologies and Clinical Practice - Design and Development of Affordable Healthcare Technologies ◽

10.4018/978-1-5225-4969-7.ch016 ◽

2018 ◽

pp. 318-337 ◽

Cited By ~ 1

Author(s):

Bikesh Kumar Singh ◽

Satya Eswari Jujjavarapu

Keyword(s):

Machine Learning ◽

Medical Images ◽

Radial Basis Function Network ◽

Objective Evaluation ◽

Disease Classification ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Future Challenges ◽

Artificial Neural Network Ann ◽

Applications Of Machine Learning

Machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), radial basis function network (RBFN), random forest (RF), naive Bayes classifier, etc. have gained much attention in recent years due to their widespread applications in diverse fields. This chapter is focused on providing a comprehensive insight of various techniques employed for key areas of medical image processing and analysis. Different applications covered in this chapter include feature extraction, feature selection, and cancer classification in medical images. The authors present current practices and evaluation measures used for objective evaluation of different machine learning methods in context to above-mentioned applications. Various factors associated with acceptance/rejection of such automated systems by medical research community are discussed. The authors also discuss how the interaction between automated analysis systems and medical professionals can be improved for its acceptance in clinical practice. They conclude the chapter by presenting research gaps and future challenges.

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