A predictive model for heart disease using clustering techniques

2017 ◽  
Vol 8 (3) ◽  
Author(s):  
A.SOW MITH ◽  
V.SUCHA RITA ◽  
P.SOW JANYA ◽  
B.GEETHA KRISHNA
Keyword(s):  
Heart Disease ◽  
Predictive Model ◽  
Clustering Techniques

scholarly journals Assessment of Heart Disease using Fuzzy Classification Techniques

2001 ◽  
Vol 1 ◽  
pp. 369-390 ◽  
Author(s):  
Horia F. Pop ◽  
Tudor L. Pop ◽  
Costel Sarbu
Keyword(s):  
Heart Disease ◽  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Fuzzy Classification ◽  
Cardiac Patients ◽  
Clustering Techniques ◽  
Fuzzy Partition ◽  
Fuzzy Partitions ◽  
Cross Classification ◽  
Different Characteristics

In this paper we discuss the classification results of cardiac patients of ischemical cardiopathy, valvular heart disease, and arterial hypertension, based on 19 characteristics (descriptors) including ECHO data, effort testings, and age and weight. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering, and a new clustering technique, fuzzy hierarchical cross-classification. The characteristics clustering techniques produce fuzzy partitions of the characteristics involved and, thus, are useful tools for studying the similarities between different characteristics and for essential characteristics selection. The cross-classification algorithm produces not only a fuzzy partition of the cardiac patients analyzed, but also a fuzzy partition of their considered characteristics. In this way it is possible to identify which characteristics are responsible for the similarities or dissimilarities observed between different groups of patients.

scholarly journals Predictive Model for Heart Disease Diagnosis Based on Multinomial Logistic Regression

2021 ◽  
Vol 50 (2) ◽  
pp. 308-318
Author(s):  
Munandar Tb Ai ◽  
Sumiati Sumiati ◽  
Vidila Rosalina
Keyword(s):  
Logistic Regression ◽  
Heart Disease ◽  
Predictive Model ◽  
Multinomial Logistic Regression ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Computational Techniques ◽  
Regression Equations ◽  
Heart Disease Diagnosis ◽  
Numeric Data

Many computational approaches are used to assist the analysis of influencing factors, as well as for the need forprediction and even classification of certain types of disease. In the case of disease classification, the data usedare often categorical data, both for dependent variables and for independent variables, which are the results ofconversion from numeric data. In other words, the data used are already unnatural. Conversion processes oftendo not have standard rules, thus affecting the accuracy of the classification results. This research was conductedto form a predictive model for heart disease diagnosis based on the natural data from the patients' medicalrecords, using the multinomial logistic regression approach. The medical record data were taken based on thepatients’ electrocardiogram information whose data had been cleansed first. Other models were also tested tosee the accuracy of the heart disease diagnosis against the same data. The results showed that multinomiallogistic regression had the highest level of accuracy compared to other computational techniques, amountingto 75.60%. The highest level of accuracy is obtained by involving all variables (based on the results of the firstexperiment). This research also produced seven regression equations to predict the heart disease diagnosisbased on the patients’ electrocardiogram data.

scholarly journals Pulmonary Hypertension (PH) Due to Left Heart Disease: A Predictive Model Using the National Echo Database of Australia (NEDA)

2017 ◽  
Vol 26 ◽  
pp. S61
Author(s):  
K. Chung ◽  
D. Playford ◽  
D. Celermajer ◽  
J. Codde ◽  
G. Scalia ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Heart Disease ◽  
Predictive Model ◽  
Left Heart ◽  
Left Heart Disease
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