Ensemble Classification for Liver Disease Prediction: A Comparative Analysis of AdaBoost and Gradient Boosting
Abstract
Liver disease is a major health concern worldwide, and early diagnosis is crucial for effective treatment and management. In this research, we employ ensemble classification techniques to predict the presence or absence of liver disease using a dataset comprising 441 male and 142 female patient records. We compare the performance of two popular ensemble algorithms, AdaBoost and Gradient Boosting, in terms of accuracy, precision, and recall. Our results demonstrate that both AdaBoost and Gradient Boosting exhibit high accuracy, precision, and recall rates, making them promising tools for liver disease prediction. This research contributes to the growing body of literature on ensemble classification methods for medical diagnosis and highlights the potential of these techniques in improving healthcare outcomes.
Keywords
Download Options
Introduction
Liver illness is a massive term that covers all of the potential issues that reason the liver to dismissal to play out its dispensed cutoff points. Liver destructive advancement is the most dangerous and undermining sicknesses in the entire world [6]. Liver destructive improvement is unyielding to perceive at the start time span considering the shortfall of appearances.
The liver standard work is to strain the blood beginning from the stomach related plot, preceding passing it to whatever is left of the body. The liver besides detoxifies fake materials and cycles drugs. As it does as needs be, the liver conceals bile that breezes up back in the retention packages. The liver also makes proteins fundamental for blood thickening and different cutoff points [6]. Liver sickness is any annoyance of liver breaking point that causes pollution.
The application of ensemble learning techniques in medical diagnosis holds great promise, with opportunities to expand datasets, incorporate additional features, and enhance model interpretability. This research contributes to the growing body of knowledge in healthcare, paving the way for more accurate and dependable diagnostic tools in the future. It reinforces the significance of ensemble classification in addressing real-world healthcare challenges and underscores the importance of continued research in this domain.
Conclusion
In this research, we have demonstrated the effectiveness of ensemble classification algorithms, namely AdaBoost and Gradient Boosting, in predicting liver disease. Both algorithms exhibited high accuracy, precision, and recall rates, highlighting their potential as valuable tools for early diagnosis and intervention in liver disease cases. The results suggest that Gradient Boosting may offer a slight advantage over AdaBoost, but both methods are reliable options for medical practitioners.