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Liver Disease Prediction using Machine Learning Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 27 |
| Year of Publication: 2023 |
| Authors: Riya, Barinderjit Kaur |
10.5120/ijca2023923022
|
Riya, Barinderjit Kaur . Liver Disease Prediction using Machine Learning Algorithms. International Journal of Computer Applications. 185, 27 ( Aug 2023), 36-44. DOI=10.5120/ijca2023923022
Abstract
Early diagnosis is essential to improving patient outcomes and decreasing the costs of healthcare. Liver disease is a big worldwide health problem, and early detection plays a critical role in both of these areas. A data-driven technique that makes use of supervised learning algorithms is presented in this research report as a method for estimating the likelihood of developing liver disease. For the objectives of research, the study makes use of a dataset collected from www.kaggle.com that contains information on the demographics, lifestyle, and medical history of 416 patients who were treated at a hospital in India. In order to construct accurate prediction models for the risk of liver illness, we use three different supervised learning techniques. These are decision trees, random forests, and logistic regressions. Accuracy, specificity, sensitivity, and the area under the receiver operating characteristic (ROC) curve are the metrics that are used in order to assess the performance of these models. According to the findings, the hybrid method surpasses the other two algorithms by obtaining higher levels of accuracy (75.7 percent), sensitivity (71.4 percent), and specificity (77.2 percent), as well as a higher area under the ROC curve (0.80). This work demonstrates the potential of supervised learning algorithms in forecasting the risk of liver disease using patient data, especially in areas where there is a limited availability of resources.
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