Abstract
The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618.
Highlights
Academic Editor: Sampath Pradeep e liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms
Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. e result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest Mean Square Error (MSE) of 0.0618
We will extract useful features that help in the detection of liver cancer with the help of feature extraction techniques in which Filter Methods, Embedded Methods, and Wrapper Methods are very helpful. ese methods will be implemented in the regression model to train our data and some useful features will be extracted from these models. ese algorithms help us in the extraction of useful data that will be useful in further treatment while diagnosing early liver disease
Summary
Academic Editor: Sampath Pradeep e liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Uncontrolled growth of harmful cells is developed inside the liver If diagnosed late, it may cause death. Ese techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. E liver has multiple functions carried out through liver cells (hepatocytes) [2] It creates half of the cholesterol of the body, and the rest of it comes from food, which will be helpful in making bile that supports digestion. Diagnosing liver diseases at an early stage will be the task with the help of soft computing techniques
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