A Fact-Based Liver Disease Prediction by Enforcing Machine Learning Algorithms

In the 21st-century, the issue of liver disease has been increasing all over the world. As per the latest survey report, liver disease death toll has been rise approximately 2 million per year worldwide. The overall percentage of death by liver disease is 3.5% worldwide. Chronic Liver disease is also considered to be one of the deadly diseases, so early detection and treatment can recover the disease easily. Due to rapid advancement in Artificial intelligence (AI), like various machine learning algorithms SVM, K-mean clustering, KNN, Random forest, Logistic regression, etc., This will improve the life span of a patient suffering from Chronic Liver Disease (CLD) in early stages. The data can be obtained in a large volume due to the broad exploitation of bar codes for supreme marketable products, the mechanization of various business and government dealings, and the development in the data collection tools. This research work is based on liver disease prediction using machine learning algorithms. Liver disease prediction has various levels of steps involved, pre-processing, feature extraction, and classification. In this s research work, a hybrid classification method is proposed for liver disease prediction. And Datasets are collected from the Kaggle database of Indian liver patient records. The proposed model achieved an accuracy of 77.58%. The proposed technique is implemented in Python with the Spyder tool and results are analyzed in terms of accuracy, precision, and recall.

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Liver Disease Prediction using Machine-Learning Algorithms

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8365.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 2532-2534

Keyword(s):

Machine Learning ◽

Liver Disease ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Disease Prediction ◽

Learning Techniques ◽

To Come ◽

Developing Pattern

machine learning is a part of man-made consciousness that utilizes an assortment of measurable, probabilistic and enhancement methods that enables PCs to "learn" from past precedents and to identify hard-to-recognize designs from huge, boisterous or complex informational indexes. This capacity is especially appropriate to restorative applications, particularly those that rely upon complex proteomic and genomic estimations. Therefore, machine learning is every now and again utilized in disease conclusion and discovery. All the more as of late machine learning has been connected to disease guess and forecast. This last mentioned approach is especially intriguing as it is a piece of a developing pattern towards customized, prescient drug. In collecting this audit we led a wide overview of the distinctive sorts of machine learning techniques being utilized, the kinds of information being coordinated and the execution of these techniques in growth forecast and visualization. Various distributed examinations additionally appear to come up short on a fitting level of approval or testing. Among the better composed and approved investigations unmistakably machine learning techniques can be utilized to generously (15-25%) enhance the precision of foreseeing disease powerlessness, repeat what's more, mortality. At a more major level, it is additionally apparent that machine learning is likewise enhancing our fundamental comprehension of disease improvement and movement.

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A Comparative Study of Two Machine Learning Algorithms for Heart Disease Prediction System

10.1109/icsgrc53186.2021.9515250 ◽

2021 ◽

Author(s):

Wan Adlina Husna Wan Azizan ◽

A'zraa Afhzan Ab Rahim ◽

Siti Lailatul Mohd Hassan ◽

Ili Shairah Abdul Halim ◽

Noor Ezan Abdullah

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Comparative Study ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Disease Prediction ◽

Prediction System

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Survey on machine learning algorithms for liver disease diagnosis and prediction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.8.9981 ◽

2018 ◽

Vol 7 (1.8) ◽

pp. 99 ◽

Cited By ~ 4

Author(s):

M Kiran Kumar ◽

M Sreedevi ◽

Y C. A. Padmanabha Reddy

Keyword(s):

Machine Learning ◽

Liver Disease ◽

Learning Algorithms ◽

Disease Diagnosis ◽

Computer Aided Diagnosis ◽

Vital Role ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Computer Aided ◽

Aided Diagnosis

Machine learning plays a vital role in health care industry. It is very important in Computer Aided Diagnosis. Computer Aided Diagnosis is a quickly developing dynamic region of research in medicinal industry. The current specialists in machine learning guarantee the enhanced precision of discernment and analysis of diseases. The computers are empowered to think by creating knowledge by learning. This procedure enables the computers to self-learn individually without being explicitly programed by the programmer .There are numerous sorts of Machine Learning Techniques and which are utilized to classify the data sets. They are Supervised, Unsupervised and Semi-Supervised, Reinforcement, deep learning algorithms. The principle point of this paper is to give comparative analysis of supervised learning algorithms in medicinal area and few of the techniques utilized as a part of liver disease prediction.

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