Liver Disease Prediction Using Machine Learning
Abstrak
Numerous causes contribute to the prevalence of liver disease, which is a major global health concern. Improved patient outcomes and prompt intervention can be facilitated by early detection and precise prediction of liver disease. Based on clinical and demographic characteristics, we present a machine learning method for predicting liver disease in this work. Patient medical histories, a range of laboratory test results, and demographic data make up the dataset used in this investigation. We use a variety of machine learning algorithms, such as support vector machines, decision trees, random forests, and logistic regression, to create prediction models. The most pertinent traits that contribute to the prediction of liver disease are found using feature selection approaches. Metrics including accuracy, precision, recall, and F1-score are used to evaluate performance. The findings show the efficiency of the suggested machine learning models in correctly identifying if liver disease is present or not. Better patient care and the efficient use of healthcare resources are made possible by this research's contribution to the development of trustworthy diagnostic instruments for the early identification and treatment of liver disease.
Penulis (7)
Madugula Anjaneyulu
G. Karuna
Bharadwaj Vedadri Yoganand
Deepthi Palakurthy
Maithili Kamalakannan
Vivek John
Alok Jain
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Total Sitasi
- 23×
- Sumber Database
- Semantic Scholar
- DOI
- 10.46632/jdaai/4/1/7
- Akses
- Open Access ✓