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Chronic Disease Prediction by Analyzing Clinical Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 12 |
| Year of Publication: 2021 |
| Authors: Pandey Himanshu Kumar Sachchidanand, Patel Jaxitkumar D., Pallavi Hire |
10.5120/ijca2021921395
|
Pandey Himanshu Kumar Sachchidanand, Patel Jaxitkumar D., Pallavi Hire . Chronic Disease Prediction by Analyzing Clinical Data. International Journal of Computer Applications. 183, 12 ( Jun 2021), 8-12. DOI=10.5120/ijca2021921395
Abstract
This paper reviews various applications of machine learning and deep learning models and concepts in the diagnosis of chronic diseases. Patients suffering from these diseases need lifelong treatment. At the moment, Predictive models are frequently applied in the diagnosis and forecasting of these chronic diseases. In this study, the most common chronic diseases are been reviewed and analysed. This paper mostly focused on chronic diseases like Diabetes, Heart Disease and Skin Diseases. The outcomes of this journal suggest the diagnosis of chronic diseases, but there is no standard method to determine the best approach in real-time medical/clinical practice since these methods have their own advantages and disadvantages. Among the most commonly used methods, this paper considered Support Vector Machines (SVM), logistic regression (LR), clustering and convolutional neural network. These models are highly applicable in the classification, and diagnosis of chronic diseases and are expected to become more important in medical practiceshortly.
References
- Artificial Intelligence (AI) “https://ai.google/”
- Support Vector Machine (SVM) “https://scikit-learn.org/stable/modules/svm.html”
- Linear Regression (LR) “https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html”
- Clustering Machine Learning “https://developers.google.com/machine-learning/clustering”
- Convolutional Neural Network (CNN) “https://www.tensorflow.org/tutorials/images/cnn”
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