CARDIOVASCULAR DISEASE PREDICTION USING GENETIC ALGORITHM AND NEURO-FUZZY SYSTEM

-Cardiovascular disease (CVD) is related to heart and a major cause of morbidity and transience in the modern society. Diagnosis of cardiovascular disease using various medical tests is an important but complicated task which should be performed accurately. Hence a powerful tool in the prediction of heart disease with lower cost has become the need of time. A very scarce number of the systems predict heart diseases based on risk factors such as age, family history, diabetes, hypertension, high cholesterol, tobacco smoking, alcohol intake, obesity or physical inactivity, etc. Heart disease patients have lot of these visible risk factors in common which can be used very effectively for diagnosis. System based on such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of heart disease even before he visits a hospital. In this paper, we will apply NFS to the dataset which is nothing but the risk factors, for prediction and training of network will be done using back propagation algorithm and weight optimization will be done by genetic algorithm. The NFS combines neuro adaptive capability and fuzzy logic reasoning for prediction. Keywords—Heart diseases risk factors, Prediction and Diagnosis systems, Genetic algorithm, NFS. Cardiovascular Disease Prediction Using Genetic Algorithm And Neuro-Fuzzy System 105 disease patients have lot of these visible risk factors in common which can be used very effectively for diagnosis. Hence in this paper we are giving a technique for prediction of heart disease using major risk factors. This technique involves neural networks and genetic algorithms. A reliable method for both feature selection and classification is required. The feature selection is based on genetic algorithm and system learning is based on neuro-fuzzy system (NFS)[2]. Feature selection is another factor that impacts classification accuracy. By extracting as much information as possible from a given data set while using the smallest number of features, we can save significant computation time and build models that generalize better for unseen data points. The hybrid system implemented uses the global optimization advantage of genetic algorithm. The learning is fast, more stable and accurate as compared to simple back propagation. The proposed system will be implemented in Matlab. Neural networks mimic our ability to adapt to circumstances and learn from past experiences, fuzzy logic addresses the imprecision or vagueness in input and output. Genetic algorithms are inspired by biological evolution, can systemize random search and reach to optimum characteristics. Each of these technologies has provided efficient solution to wide range of problems belonging to different domains. However, each of these technologies has advantages and disadvantages. It is therefore appropriate that Hybridization of these three technologies is done so as to overcome the weakness of one with the strength of other. The main objective of this research is to develop a prototype Intelligent Heart Disease Prediction System with NFS and genetic algorithm using historical heart disease databases to make intelligent clinical decisions which traditional decision support systems cannot. The NFS model integrates adaptable fuzzy inputs with a modular neural network to rapidly and accurately approximate complex functions. Fuzzy inference systems are also valuable, as they combine the explanatory nature of rules (MFs) with the power of neural networks [1]. The following is an overview of how our system will work is shown in fig. 1. Figure. 1 System Flow Diagram The user input will be fed to the system. The system will accurately predict the user’s possibility of contracting a heart disease. In order to train the proposed system the input needed would have to be a comprehensive dataset. This will be obtained from the Cleveland University database which is freely available for use. The dataset will be further compacted and processed by taking 14 precise parameters that contribute as the risk factors for heart diseases. The dataset to be used is represented in Fig. 2. Figure. 2 Dataset representation The output that would be shown to the user is proposed to be of the following format as shown in fig. 3. The user would insert the values for the required parameters and submit to the system for prediction purpose. Sneha Nikam, Priyanshi Shukla and Megh Shah 106 Figure. 3 System accepting user input The system predicts the possibility of a person being prone to heart disease. The system also depicts a graph stating the risk percent that the user faces based on the predictive evaluations [Fig. 4]. Figure. 4 System displaying predicted results The system would be bound by accuracy constraints at preliminary levels which would be overcome as the system intercepts more and more distinguished inputs. Thereby enabling further system learning. III. IMPLEMENTATION The implementation would be done with the help of feature selection and a sequential execution of algorithms namely; Genetic Algorithm followed by system learning implemented via Neuro-Fuzzy system.The final system would be implemented as shown in Fig. 5. Cardiovascular Disease Prediction Using Genetic Algorithm And Neuro-Fuzzy System 107 Figure. 5 Flow Chart of proposed work Feature selection reduces the dimensionality of data by selecting only a subset of measured features (predictor variables) to create a model. Reducing the number of features (dimensionality) is important in statistical learning. For many data sets with a large number of features and a limited number of observations, such as bioinformatics data, usually many features are not useful for producing a desired learning result and the limited observations may lead the learning algorithm to over fit to the noise. Reducing features can also save storage and computation time and increase comprehensibility.GA is basically a domain independent search technique, they are ideal for applications where domain knowledge and theory is difficult or impossible to provide. The GA combines selection, crossover, and mutation operators with the goal of finding the best solution to a problem by searching until the specified criterion is met. The solution to a problem is called a chromosome, which is composed of a collection of genes. In hybrid neuro-fuzzy genetic applications. The GA creates an initial population and then evaluates this population by training a network for each chromosome. It then evolves the population through multiple generations in the search for the best network parameters. GA causes the initial population to evolve towards a population that is expected to contain the best solution. A fitness function is needed in the Genetic Algorithm to evaluate whether an individual is “fit” to survive. The principle is that individuals with high classification accuracy and small number of features has a high fitness value, and thus high probability to be pass its genes to the next generation. A single objective fitness function that combines the two goals into one was designed to solve the multiple criteria problem. The formula is given in (1).