Data Mining Techniques for Optimization of Liver Disease Classification

2013 ◽  
Author(s):  
Sadiyah Noor Novita Alfisahrin ◽  
Teddy Mantoro
Keyword(s):  
Data Mining ◽  
Liver Disease ◽  
Disease Classification ◽  
Data Mining Techniques

scholarly journals Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm

2012 ◽  
Vol 39 (7Part1) ◽  
pp. 4255-4264 ◽  
Author(s):  
U. Rajendra Acharya ◽  
S. Vinitha Sree ◽  
Ricardo Ribeiro ◽  
Ganapathy Krishnamurthi ◽  
Rui Tato Marinho ◽  
...  
Keyword(s):  
Data Mining ◽  
Feature Extraction ◽  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Disease Classification ◽  
Hybrid Feature Extraction

scholarly journals Liver Disease Prediction and Diagnosis Expert System using Data Mining Techniques

2019 ◽  
Vol 7 (3) ◽  
pp. 1406-1410
Author(s):  
Dr. N. V. Ramana Murthy ◽  
S. Shruti, ◽  
Vinay Bhargav V. ◽  
Anil Kumar S.
Keyword(s):  
Data Mining ◽  
Liver Disease ◽  
Expert System ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Liver Disease Classification using Deep Learning Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 5105-5111 ◽  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Liver Disease ◽  
Learning Algorithm ◽  
Liver Disorder ◽  
Disease Classification ◽  
Human Culture ◽  
Deep Learning Algorithm

Data Mining is one of the prevalent elucidating portions of programmed request and distinguishing proof. It involves data mining counts and strategies to examine helpful data. Of late, liver dissents have disproportionately expanded and liver infections are complimenting one of the most human pains in different countries. Early assurance of Liver Disorder is essential for the welfare of human culture. This complaint should be considered sincerely by setting up watchful structures for the early break down and expectation of Liver contaminations. The robotized gathering system suffers with non attendance of precision results when differentiated and cautious biopsy. We propose another model for liver issue request for separating the patient's helpful, data using ANN algorithm. The remedial records are organized whether there is a believability of essence of disorder or not. This proposed methodology uses extracted features using M-PSO and ANN for classifying the features. The ANN methodology improves the accuracy when appeared differently in relation to existing request computations. This paper focuses classification of selected features for classification.

scholarly journals Application of Data Mining Techniques to Explore Predictors of HCC in Egyptian Patients with HCV-related Chronic Liver Disease

2015 ◽  
Vol 16 (1) ◽  
pp. 381-385 ◽  
Author(s):  
Dalia Abd El Hamid Omran ◽  
AbuBakr Hussein Awad ◽  
Mahasen Abd El Rahman Mabrouk ◽  
Ahmad Fouad Soliman ◽  
Ashraf Omar Abdel Aziz
Keyword(s):  
Data Mining ◽  
Liver Disease ◽  
Chronic Liver Disease ◽  
Chronic Liver ◽  
Data Mining Techniques ◽  
Egyptian Patients

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

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