A Predictive Model for Heart Disease Diagnosis Using Fuzzy Logic and Decision Tree

2019 ◽  
pp. 131-140 ◽  
Author(s):  
Asim Kumar Pathak ◽  
J. Arul Valan
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Decision Tree ◽  
Predictive Model ◽  
Disease Diagnosis ◽  
Heart Disease Diagnosis

Heart Disease Diagnosis System Using Fuzzy Logic

2018 ◽  
Author(s):  
Hasan Kahtan ◽  
Kamal Z. Zamli ◽  
Wan Nor Ashikin Wan Ahmad Fatthi ◽  
Azma Abdullah ◽  
Mansoor Abdulleteef ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Diagnosis System ◽  
Heart Disease Diagnosis

An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

2016 ◽  
Vol 26 (04) ◽  
pp. 1750061 ◽  
Author(s):  
G. Thippa Reddy ◽  
Neelu Khare
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Fuzzy System ◽  
Disease Diagnosis ◽  
Fuzzy Logic System ◽  
Classification Model ◽  
Rule Based ◽  
Diagnosis System ◽  
Heart Disease Diagnosis ◽  
Logic System

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

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 A Novel Approach for Heart Disease Diagnosis using Data Mining and Fuzzy Logic

2012 ◽  
Vol 54 (17) ◽  
pp. 16-21 ◽  
Author(s):  
Nidhi Bhatla ◽  
Kiran Jyoti
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Novel Approach ◽  
Using Data ◽  
Heart Disease Diagnosis

Heart disease diagnosis based on mediative fuzzy logic

2018 ◽  
Vol 89 ◽  
pp. 51-60 ◽  
Author(s):  
Ion Iancu
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Heart Disease Diagnosis

Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis

2019 ◽  
Vol 13 (2) ◽  
pp. 185-196 ◽  
Author(s):  
G. Thippa Reddy ◽  
M. Praveen Kumar Reddy ◽  
Kuruva Lakshmanna ◽  
Dharmendra Singh Rajput ◽  
Rajesh Kaluri ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Heart Disease ◽  
Hybrid Genetic Algorithm ◽  
Disease Diagnosis ◽  
Heart Disease Diagnosis
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