IUP Publications Online
Home About IUP Magazines Journals Books Archives
     
Recommend    |    Subscriber Services    |    Feedback    |     Subscribe Online
 
The IUP Journal of Computer Sciences :
Cardiovascular Disease Prediction Using Fuzzy Logic Expert Systems
:
:
:
:
:
:
:
:
:
 
 
 
 
 
 
 

Cardiovascular disease is a term used to describe a variety of heart diseases, illnesses and events that affect the heart and circulatory system. The aim of this study is to design a fuzzy expert system for heart disease diagnosis. The neuro-fuzzy system was designed with eight input fields and one output field. The input variables are heart rate, blood pressure, age, cholesterol, chest pain type, blood sugar, exercise and sex. The output detects the risk levels of patients, which are classified into four different categories: very low, low, high and very high. The dataset used was extracted from the UCI machine learning repository and preprocessed in order to make it appropriate for the training; then the initial FIS was generated. The network was trained with the set of training data, after which it was tested and validated with the set of testing data. The output of the system was designed in such a way that the patient could use it personally. The patient just needs to supply appropriate values which serve as input to the system, and based on the values supplied, the system will be able to predict the risk level of the patients. Fuzzy Interference System (FIS) is a combination of neural networks and adaptive neuro fuzzy. The results obtained from the system are compared with the SVM classifier. The system has been tested and the result showed over 90% accuracy. It has been shown that neuro-fuzzy is suitable and feasible to be used as a supportive tool for disease diagnosis.

 
 
 

Big data is an emerging area which handles large collections of voluminous complex data. Big data may be both structured and unstructured data. These data are not easily processed using traditional methods. Traditional databases handle only structured and limited amount of data because they are centralized (Zhongheng, 2014). The unstructured data sources used for big data analytics may not fit in the traditional data warehouses. Furthermore, traditional data warehouses may not be able to handle the processing demands posed by big data. As a result, a new class of big data technology has emerged and is being used in many big data analytics environments. Big data analytics is often associated with cloud computing because the analysis of large datasets in real-time requires a platform like Hadoop to store large datasets across a distributed cluster and Map Reduce to coordinate, combine and process data from multiple sources (Health Forum). Although the demand for big data analytics is high, there is currently a shortage in data healthcare analytics, and of scientists and other analysts who have experience working with big data in a distributed, open source environment. Appropriate computer-based information and/or decision support systems can aid in achieving clinical tests at a reduced cost. This project aimed at analyzing different predictive/descriptive analytic techniques proposed for the diagnosis of heart disease.

 
 
 

Computer Sciences IUP Journal , Cardiovascular disease, FIS, Fuzzy logic, Fuzzy expert system