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Dharani, D.
- A Review of Data Classification Using various Classifiers Algorithm
Authors
1 Department of IT, PSG College of Technology Coimbatore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 12, No 5 (2020), Pagination: 81-85Abstract
Machine-Learning (ML) methods have great importance in interdisciplinary domains. Besides many areas, healthcare domain is the most thriving area where the involvement of Machine Learning algorithms is relatively essential. The purpose of this research is to put together the various supervised learning algorithms such as Logistic Regression, Random Forest, XG boost and Support Vector Machine for the prediction of heart disease by considering relevant medical parameters in the dataset. It uses the training dataset to get better boundary conditions which could be used to determine each target class. Once the boundary conditions are determined, the validation will be done to predict the target class.The system also analyses the performance metrics of the algorithms in order to compare their effectiveness in real-time.Keywords
Healthcare Domain, Heart Disease, Supervised Learning Algorithms, Performance Analysis.- A Review on Heart Disease Prediction Using Supervised Learning Techniques
Authors
1 Department of IT, PSG College of Technology Coimbatore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 12, No 4 (2020), Pagination: 61-64Abstract
Nowadays Health care System using Internet of Things (IoT) provides better efficiency than the traditional health care systems. Health care using IoT provides the easiest way of communication between patients and doctors. The patient`s health is monitored continuously by the doctor through IoT devices which in turn produces the data pertaining to patient`s health. According to the data received from the IoT devices, the doctors can make a diagnosis of patient health on Real-time. It can be done through Machine Learning (ML) algorithms. This ML technique helps to minimize the disease recurrence by alerting the doctor by identifying the risk factors of a patient`s health. The system uses various supervised learning algorithms such as Logistic Regression, Support Vector Machine, Random Forest, XG Boost Algorithms for disease diagnosis and prediction. The algorithms are then compared using evaluation metrics.