Analysis of Liver Disease Using Deep Learning Methodology

A data analysis report to optimize the hotel's dwindling profit and lower reservation cancellations.

ABSTRACT:

The liver serves as one of the most significant organs in the human body. Nowadays, alcohol consumption, tattoos or body piercings, drugs, and obesity are the main causes of liver disease. The liver is in charge of producing bile, which is a fluid that aids digestion. The red blood cells will disintegrate, balance, and produce the nutrients as the blood exits the stomach and intestine and travels through the liver. These are the main processes that are carried out inside the body. If these fail to happen, then it will lead to many problems inside the body. So, it’s important to see that the functions of the liver are at a normal level. Bilirubin, Albumin, Proteins, AST, ALT, and ALP are the liver function tests that help to check whether the liver has a normal range or an abnormal range. We can predict liver disease in a patient at an early stage based on previous predicted values using data from patients with abnormal liver function. This will be helpful for the doctors to make a diagnosis. In this paper, the liver function test is analysed for predicting liver disease, where the input of the patients and the output data are passed into various classifiers such as Support Vector Machine, K-Nearest Neighbor, Hard Voting Classifier, and Deep Neural Network Multilayer Perceptron techniques for predicting the liver health of patients, and optimization techniques such as the Confusion Matrix, Precision Score, Recall, Accuracy, Specificity, and F-score are used to determine which model is the best. The study shows that the voting classifier is the best for this dataset. It is the most accurate and simple method for detecting liver disease in humans.

Material and Methods

Data Collection

Methodology

Data Processing

a) Data Cleaning b) Data Standardization

K-Nearest Neighbours (KNN)

Support Vector Machines (SVM)

Multilayer Perceptron Neural Network (MLP)

Hard Voting Classifier (HVC)

Result and findings:

Models	Accuracy	Specificity	F-score	Precision	Recall
MLP	0.77241379	1	0.87159533	0.7724138	1
SVM	0.76551724	0.9642857	0.864	0.7826087	0.964286
KNN	0.73103448	0.8928571	0.83682008	0.7874016	0.892857
HVC	0.7862069	0.9464286	0.87242798	0.8091603	0.946429

June 2023 – Juily 2023