scholarly journals The Diagnosis of Chronic Liver Disease using Machine Learning Techniques

2021 ◽  
Vol 9 (2) ◽  
pp. 554-564
Author(s):  
Golmei Shaheamlung, Harshpreet Kaur
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Chronic Liver Disease ◽  
Learning Algorithms ◽  
Research Work ◽  
Chronic Liver ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Survey Report

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.  

scholarly journals Liver Disease Prediction using Machine-Learning Algorithms

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.

scholarly journals Survey on machine learning algorithms for liver disease diagnosis and prediction

2018 ◽  
Vol 7 (1.8) ◽  
pp. 99 ◽  
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.

scholarly journals Prediction and Analysis of Student Performance in Secondary Education Based on Data Mining and Machine Learning Techniques

2020 ◽  
pp. 294-301
Author(s):  
Meenal Joshi ◽  
Shiv Kumar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Secondary School ◽  
Student Performance ◽  
Learning Algorithms ◽  
Research Work ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Student’S Performance

<p>According to modern era education is the key to achieve success in the future; it develops a human personality, thoughts, and social skills. The purpose of this research work is to focus on educational data mining (EDM) through machine learning algorithms. EDM means to discover hidden knowledge and pattern about student's performance. Machine learning can be useful to predict the learning outcomes of students. From last few years, several tools have been used to judge the student's performance from different points of view like the student's level, objectives, techniques, algorithms, and different methods. In this paper, predicting and analyzing student performance in secondary school is conducted using data mining techniques and machine learning algorithms such as Naive Bayes, Decision Tree algorithm J48, and Logistic Regression. For this the collection of dataset from "Secondary School" and then filtration is applying on desired values using WEKA, tool.</p>

scholarly journals Prediction of Liver Disease using Machine Learning Algorithms

2020 ◽  
Vol 9 (9) ◽  
pp. 57-59
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Good Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Medical Field ◽  
Research Papers ◽  
Use Of Data ◽  
Learning Techniques

There are different machine learning techniques widely used in medical field to diagnosis and to predict the liver disease. To endorse the analysis of high and multi dimensional data in health care industry we have reviewed various research papers in which we have focused on various Data mining methods for making use of data in regard to this we come out with, assessment for chosen research papers. Hence, the Objective of this study is to improve the diagnosis and Prediction of the liver disease with the machine learning algorithms. In our paper we suggested that hybrid of Decision Tree and Navie Bayes can give better result with good accuracy.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

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

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

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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