scholarly journals Study and Analysis of Medical Data Mining Techniques in Healthcare for Heart Disease Using Hybrid Approach

2020 ◽  
Author(s):  
Khodke harish Eknath ◽  
Yadav S K ◽  
Kyatanavar D N
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Research Work ◽  
Hybrid Approach ◽  
Medical Data ◽  
Data Sets ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Inductive Type ◽  
The Right

Information mining frameworks are exhaustively used in coronary affliction for affirmation and figure. As heart condition is that the essential clarification for death for individuals, recognizing confirmation . The work proposed is inductive type and needs deep analysis of the data to ensure the right predictions on the data sets provided. A sample dataset of patients for heart disease will be collected from repository. It involves the steps and procedure. The proposed research work can be carried out step by step to conclude it with the accurate results.

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.

scholarly journals Improved Classification Techniques to Predict the Co-disease in Diabetic Mellitus Patients using Discretization and Apriori Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 730-733
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Census Data ◽  
Early Stage ◽  
Research Work ◽  
Numerical Data ◽  
Medical Data ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Data Set

The demand for data mining is now unavoidable in the medical industry due to its various applications and uses in predicting the diseases at the early stage. The methods available in the data mining theories are easy to extract the useful patterns and speed to recognize the task based outcomes. In data mining the classification models are really useful in building the classes for the medical data sets for future analysis in an accurate way. Besides these facilities, Association rules in data mining are a promising technique to find hidden patterns in a medical data set and have been successfully applied with market basket data, census data and financial data. Apriori algorithm, is considered to be a classic algorithm, is useful in mining frequent item sets on a database containing a large number of transactions and it also predicts the relevant association rules. Association rules capture the relationship of items that are present in data sets and when the data set contains continuous attributes, the existing algorithms may not work due to this, discretization can be applied to the association rules in order to find the relation between various patterns in data set. In this paper of our research, using Discretized Apriori the research work is done to predict the by-disease in people who are found with diabetic syndrome; also the rules extracted are analyzed. In the discretization step, numerical data is discretized and fed to the Apriori algorithm for better association rules to predict the diseases.

A Hybrid Approach for Heart Disease Prediction

2021 ◽  
pp. 95-101
Author(s):  
Aamir Khan ◽  
Dr. Sanjay Jain
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Heart Disease ◽  
Random Forest ◽  
Research Work ◽  
Hybrid Approach ◽  
Disease Prediction ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Analysis Of Performance

The data mining (DM) is a process that deals with mining of valuable information from the rough data. The method of prediction analysis (PA) is implemented for predicting the future possibilities on the basis of current information. This research work is planned on the basis of predicting the heart disease. The coronary disorder can be forecasted in different phases in which pre-processing is done, attributes are extracted and classification is performed. The hybrid method is introduced on the basis of RF and LR.The Random Forest classification is adopted to extract the attributes and the classification process is carried out using logistic regression. The analysis of performance of introduced system is done with regard to accuracy, precision and recall. It is indicated that the introduced system will be provided accuracy approximately above 90% while predicting the heart disease.

Heuristics in Medical Data Mining

2011 ◽  
pp. 1723-1726
Author(s):  
Susan E. George
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Health Care Providers ◽  
Medical Data ◽  
Data Sets ◽  
Medical Data Mining ◽  
Privacy And Security ◽  
Coding Schemes ◽  
Time Space ◽  
Complex Data Sets

Deriving—or discovering—information from data has come to be known as data mining. Within health care, the knowledge from medical mining has been used in tasks as diverse as patient diagnosis (Brameier et al., 2000; Mani et al., 1999; Cao et al., 1998; Henson et al., 1996), inventory stock control (Bansal et al., 2000), and intelligent interfaces for patient record systems (George at al., 2000). It has also been a tool of medical discovery itself (Steven et al., 1996). Yet, it remains true that medicine is one of the last areas of society to be “automated,” with a relatively recent increase in the volume of electronic data, many paper-based clinical record systems in use, a lack of standardisation (for example, among coding schemes), and still some reluctance among health-care providers to use computer technology. Nevertheless, the rapidly increasing volume of electronic medical data is perhaps one of the domain’s current distinguishing characteristics, as one of the last components of society to be “automated.” Data mining presents many challenges, as “knowledge” is automatically extracted from data sets, especially when data are complex in nature, with many hundreds of variables and relationships among those variables that vary in time, space, or both, often with a measure of uncertainty, as is common within medicine. Cios and Moore (2001) identified a number of unique features of medical data mining, including the use of imaging and need for visualisation techniques, the large amounts of unstructured nature of free text within records, data ownership and the distributed nature of data, the legal implications for medical providers, the privacy and security concerns of patients requiring anonymous data used, where possible, together with the difficulty in making a mathematical characterisation of the domain. Strictly speaking, many ventures within medical data mining are better described as exercises in “machine learning,” where the main issues are, for example, discovering the complexity of relationships among data items, or making predictions in light of uncertainty, rather than “data mining,” in large, possibly distributed, volumes of data that are also highly complex. Large data sets mean not only increased algorithmic complexity but also often the need to employ special-purpose methods to isolate trends and extract “knowledge” from data. However, medical data frequently provide just such a combination of vast (often distributed) complex data sets.

scholarly journals Improved Classification Techniques to Predict the Co-disease in Diabetic Mellitus Patients using Discretization and Apriori Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 730-733
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Census Data ◽  
Early Stage ◽  
Research Work ◽  
Numerical Data ◽  
Medical Data ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Data Set

The demand for data mining is now unavoidable in the medical industry due to its various applications and uses in predicting the diseases at the early stage. The methods available in the data mining theories are easy to extract the useful patterns and speed to recognize the task based outcomes. In data mining the classification models are really useful in building the classes for the medical data sets for future analysis in an accurate way. Besides these facilities, Association rules in data mining are a promising technique to find hidden patterns in a medical data set and have been successfully applied with market basket data, census data and financial data. Apriori algorithm, is considered to be a classic algorithm, is useful in mining frequent item sets on a database containing a large number of transactions and it also predicts the relevant association rules. Association rules capture the relationship of items that are present in data sets and when the data set contains continuous attributes, the existing algorithms may not work due to this, discretization can be applied to the association rules in order to find the relation between various patterns in data set. In this paper of our research, using Discretized Apriori the research work is done to predict the by-disease in people who are found with diabetic syndrome; also the rules extracted are analyzed. In the discretization step, numerical data is discretized and fed to the Apriori algorithm for better association rules to predict the diseases.

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.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2014 ◽  
pp. 327-360 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

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