scholarly journals Limbless Medical Data Analyzing using CRISP Model a Case Study of UK Limbless Patients

2018 ◽  
Vol 7 (4.19) ◽  
pp. 806
Author(s):  
Zahraa Shams Alden ◽  
Ayad Hameed Mousa
Keyword(s):  
Data Mining ◽  
Medical Data ◽  
Healthcare Sector ◽  
Data Sets ◽  
Significant Information ◽  
Raw Data ◽  
Medical Issues ◽  
Important Relationship ◽  
Future Events ◽  
Using Data

Data mining, usually known as knowledge elicitation in the field of computer science databases, is the procedure to find out an important relationship, useful patterns in a huge amount of raw data. Besides, many sectors have adapted and used data mining in their applications such as healthcare and industry sector. In the healthcare sector, data mining can help in determining the probability of particular health cases in medical issues which the related variables pre-known as well as predicting future events. The availability of medical data for data mining usually exist in a raw data format, therefore, it needs for making ready and exploration to be willing to use. In the context of this paper, an analyzing of medical data was introduced to support prosthetics service centers to analyze find out the significant information from limbless medical cases, besides, in providing a comprehensive understanding of amputation and its types as well as the level of amputation. To ensure extract meaningful information from the intended data sets as well as to follow a systematic approach, the CRISP-DM model was adopted. The findings show the important and meaningful of the analyzing data using data mining modes. 

scholarly journals Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

2021 ◽  
pp. 73-79
Author(s):  
G. Ramadevi ◽  
Srujitha Yeruva ◽  
P. Sravanthi ◽  
P. Eknath Vamsi ◽  
S. Jaya Prakash
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Medical Data ◽  
Healthcare Sector ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Efficiency Comparison ◽  
Using Data ◽  
Mining Algorithms

In a digitized world, data is growing exponentially and it is difficult to analyze the data and give the results. Data mining techniques play an important role in healthcare sector - BigData. By making use of Data mining algorithms it is possible to analyze, detect and predict the presence of disease which helps doctors to detect the disease early and in decision making. The objective of data mining techniques used is to design an automated tool that notifies the patient’s treatment history disease and medical data to doctors. Data mining techniques are very much useful in analyzing medical data to achieve meaningful and practical patterns. This project works on diabetes medical data, classification and clustering algorithms like (OPTICS, NAIVEBAYES, and BRICH) are implemented and the efficiency of the same is examined.

scholarly journals Effectiveness of Support Vector Machines in Medical Data mining

2015 ◽  
Vol 11 (1) ◽  
pp. 25 ◽  
Author(s):  
Padmavathi Janardhanan ◽  
Heena L. ◽  
Fathima Sabika
Keyword(s):  
Data Mining ◽  
Medical Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Medical Data Mining ◽  
Rbf Network ◽  
Vector Machines ◽  
Hidden Knowledge ◽  
Using Data

The idea of medical data mining is to extract hidden knowledge in medical field using data mining techniques. One of the positive aspects is to discover the important patterns. It is possible to identify patterns even if we do not have fully understood the casual mechanisms behind those patterns. In this case, data mining prepares the ability of research and discovery that may not have been evident. This paper analyzes the effectiveness of SVM, the most popular classification techniques in classifying medical datasets. This paper analyses the performance of the Naïve Bayes classifier, RBF network and SVM Classifier. The performance of predictive model is analysed with different medical datasets in predicting diseases is recorded and compared. The datasets were of binary class and each dataset had different number of attributes. The datasets include heart datasets, cancer and diabetes datasets. It is observed that SVM classifier produces better percentage of accuracy in classification. The work has been implemented in WEKA environment and obtained results show that SVM is the most robust and effective classifier for medical data sets.

Mining Environmental Data in the ADMIRE Project Using New Advanced Methods and Tools

2012 ◽  
pp. 296-308
Author(s):  
Ondrej Habala ◽  
Martin Šeleng ◽  
Viet Tran ◽  
Branislav Šimo ◽  
Ladislav Hluchý
Keyword(s):  
Data Mining ◽  
Environmental Data ◽  
Environmental Applications ◽  
Data Sets ◽  
Distributed Data ◽  
New Methods ◽  
Prospective Application ◽  
Using Data ◽  
Computer Power

The project Advanced Data Mining and Integration Research for Europe (ADMIRE) is designing new methods and tools for comfortable mining and integration of large, distributed data sets. One of the prospective application domains for such methods and tools is the environmental applications domain, which often uses various data sets from different vendors where data mining is becoming increasingly popular and more computer power becomes available. The authors present a set of experimental environmental scenarios, and the application of ADMIRE technology in these scenarios. The scenarios try to predict meteorological and hydrological phenomena which currently cannot or are not predicted by using data mining of distributed data sets from several providers in Slovakia. The scenarios have been designed by environmental experts and apart from being used as the testing grounds for the ADMIRE technology; results are of particular interest to experts who have designed them.

Competitive Advantage and Automated Sharing of Tacit Knowledge

2018 ◽  
pp. 1122-1133
Author(s):  
Michael A. Chilton ◽  
James M. Bloodgood
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Competitive Advantage ◽  
Tacit Knowledge ◽  
Data Sets ◽  
Massive Data Sets ◽  
Raw Data ◽  
Competitive Position ◽  
Individual Preferences ◽  
Automated Methods

In this chapter, the authors investigate how raw data, obtained from a variety of sources, can be processed into knowledge using automated techniques that will help organizations gain a competitive advantage. Firms have amassed so much data that only automated methods, such as data mining or converting existing knowledge into expert systems is possible to make any sense of it or to protect it from competitors. Further, the data that is processed may be considered tacit knowledge because it is hidden from people until it is processed. In this chapter, the authors discuss various sources of data that might help an organization achieve and sustain a competitive advantage. A firm can data mine its own production database for insight regarding its customers and markets that have previously been ignored. It might also mine social media (e.g., Facebook and Twitter), which has become a forum for individual preferences and activities from which the savvy organization could turn into competitive advantage. They also discuss how this knowledge can be protected from intrusion by competitors to sustain the competitive position it may achieve as a result of the discovery of knowledge from massive data sets.

scholarly journals Application of Data Mining Techniques for Medical Data Classification: A Review

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Past Research ◽  
Medical Data ◽  
Extensive Literature ◽  
Literature Analysis ◽  
Data Mining Techniques ◽  
Medical Data Classification ◽  
Using Data

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

Death and Morbidity Prediction Using Data Mining in Perforated Peptic Ulcers

2020 ◽  
Vol 9 (1) ◽  
pp. 37-49
Author(s):  
Hugo Peixoto ◽  
Lara Silva ◽  
Soraia Pereira ◽  
Tiago Jesus ◽  
Vitor Neves Lopes ◽  
...  
Keyword(s):  
Data Mining ◽  
Biochemical Parameters ◽  
Scoring Systems ◽  
Medical Data ◽  
Mortality Prediction ◽  
The Other ◽  
Peptic Ulcers ◽  
Common Complication ◽  
Gastrointestinal Mucosa ◽  
Using Data

Peptic ulcers are not the most common complication in gastrointestinal mucosa, but these defects stand out as being the complication with the highest mortality rate. Several scoring systems based on clinical and biochemical parameters, such as the Boey and PULP scoring system have been developed to predict the probability of mortality. In this study, a data mining process is performed in the medical data available, in order to evaluate how the scoring systems perform when trying to predict mortality and patients' state complication. Furthermore, the presented paper studies the two scoring systems presented to define which one outperforms the other. On one hand PULP scoring allows a better mortality prediction achieving, above a 90% accuracy. One the other hand, regarding complications, the Boey system achieves better results leading to a better prediction when it comes to predicting patients' state complication.

Bio-Inspired Algorithms for Medical Data Analysis

2018 ◽  
pp. 251-275 ◽  
Author(s):  
Hanane Menad ◽  
Abdelmalek Amine
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Social Behavior ◽  
Medical Data ◽  
The Other ◽  
Data Sets ◽  
Classification Rules ◽  
Medical Data Mining ◽  
Good Efficiency

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for clinical diagnosis. Bio-inspired algorithms is a new field of research. Its main advantage is knitting together subfields related to the topics of connectionism, social behavior, and emergence. Briefly put, it is the use of computers to model living phenomena and simultaneously the study of life to improve the usage of computers. In this chapter, the authors present an application of four bio-inspired algorithms and meta heuristics for classification of seven different real medical data sets. Two of these algorithms are based on similarity calculation between training and test data while the other two are based on random generation of population to construct classification rules. The results showed a very good efficiency of bio-inspired algorithms for supervised classification of medical data.

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