Statlog Heart Research Articles

Early heart disease prediction using feature engineering and machine learning algorithms

Heart disease is one of the most widespread global health issues, it is the reason behind around 32 % of deaths worldwide every year. The early prediction and diagnosis of heart diseases are critical for effective treatment and sickness management. Despite the efforts of healthcare professionals, cardiovascular surgeons and cardiologists' misdiagnosis and misinterpretation of test results may happen every day. This study addresses the growing global health challenge raised by Cardiovascular Diseases (CVDs), which account for 32 % of all deaths worldwide, according to the World Health Organization (WHO). With the progress of Machine Learning (ML) and Deep Learning (DL) techniques as part of Artificial Intelligence (AI), these technologies have become crucial for predicting and diagnosing CVDs. This research aims to develop an ML system for the early prediction of cardiovascular diseases by choosing one of the powerful existing ML algorithms after a deep comparative analysis of several. To achieve this work, the Cleveland and Statlog heart datasets from international platforms are used in this study to evaluate and validate the system's performance. The Cleveland dataset is categorized and used to train various ML algorithms, including decision tree, random forest, support vector machine, logistic regression, adaptive boosting, and K-nearest neighbors. The performance of each algorithm is assessed based on accuracy, precision, recall, F1 score, and the Area Under the Curve metrics. Hyperparameter tuning approaches have been employed to find the best hyperparameters that reflect the optimal performance of the used algorithms based on different evaluation approaches including 10-fold cross-validation with a 95 % confidence interval. The study's findings highlight the potential of ML in improving the early prediction and diagnosis of cardiovascular diseases. By comparing and analyzing the performance of the applied algorithms on both the Cleveland and Statlog heart datasets, this research contributes to the advancement of ML techniques in the medical field. The developed ML system offers a valuable tool for healthcare professionals in the early prediction and diagnosis of cardiovascular diseases, with implications for the prediction and diagnosis of other diseases as well.

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel.

Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient's heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.

Comprehensive analysis of supervised algorithms for coronary artery heart disease detection

AbstractThe mortality rate of cardiovascular disease is increasing at an alarming rate and affects more than 31% of the world's population. The increment in obesity, strain, high blood pressure, concern, high body mass index, depression, high glucose level, and high cholesterol are the key factors that increase the risk of cardiovascular disease. Coronary heart and cerebrovascular diseases cause 80% of cardiovascular diseases with a higher mortality rate, out of which Coronary Artery Heart Disease (CAHD) causes 25% of the fatality. CAHD fatality can be minimized by early diagnosis. However, due to common symptoms of the non‐fatal disease, CAHD is often ignored by the patients and the health care team. In this paper, the authors comprehensively analysed and compared the two supervised Machine Learning models, i.e., Logistic Regression (LR) and XGBoost, for the early diagnosis of CAHD. The models are applied to a benchmark dataset (Statlog heart disease dataset). The model's parameters are also optimized by performing the Random SearchCV hyperparameter tuning. The comprehensive study is performed on both non‐optimized and optimized models as well as compared to the other well‐known existing models. After parametric optimization, both the models exhibit higher accuracy, i.e., LR 87.78% and XGBoost 91.85%, compared to the non‐optimized techniques, i.e., LR = 83.33% and XGBoost = 77.77%.

Heart disease prediction using scaled conjugate gradient backpropagation of artificial neural network

Heart disease is a deadly disease in human life. The mortality rate from any disease is the highest in the world. Therefore, before reaching the final stage of this heart disease, all precautionary measures must be taken. For this reason without the help of any kind of traditional methods, if we can scientifically diagnose heart disease at an early stage through various decision support systems, then surely death rate of this disease will decrease in the whole world. Many researchers investigate the diagnosis of heart disease by creating various intelligent medical decision support systems. Artificial neural network concepts represent the highest predictive accuracy over medical data compared to other decision support systems. In this paper, we propose a better prediction method for the existence of heart disease through the scaled conjugate gradient backpropagation of artificial neural networks using K-fold cross-validation. For cardiac datasets, the University of California Irvine (UCI) Machine Learning Repository and IEEE data port have been used. For Cleveland processed heart dataset, the proposed system uses 13 input attributes and provides minimum 63.3803% and maximum 100% accurate results; similarly, for Cleveland Hungarian Statlog heart dataset, the proposed system uses 11 input attributes and provides minimum 88.4754% and maximum 100% accurate results by estimating the presence and absence of heart disease during testing.

A Framework to Classify Clinical Data Using a Genetic Algorithm and Artificial Flora-Optimized Neural Network

A new classification framework for a Clinical Decision Support System, utilizing a Genetic algorithm and an Artificial Flora Optimized Neural Network is presented in this paper. GAFON is an artificial neural network whose topology is optimized with Genetic Algorithm and the learnable parameters are optimized with Artificial Flora Optimization algorithm. Drop out technique is used in the topology optimization phase and weight regularization is used in the parameter optimization phase. The proposed method minimizes the co-adaptation problem, reduces over-fitting of training data and improves the generalization of a feed forward neural network. The classification framework developed has been tested for classifying both multi class and binary class clinical datasets. The proposed method attained accuracy values of 86.82% for Hepatitis C Virus (HCV) for Egyptian patients, 84.91% for Vertebral Column 95.65% for Statlog Heart Disease (SHD), SHD and 93.79% for Early Stage Diabetes Risk Prediction (ESDRP), all datasets obtained from UCI repository

Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.

Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation

Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya–SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya–SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya–SCA algorithm attained the maximum value of F-measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner.

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

A Novel Fuzzy Linguistic Fusion Approach to Naive Bayes Classifier for Decision Making Applications

Naive Bayes is one of the most widely used classifier algorithms in various data mining problems. The performance of the Naí¯ve Bayes Classifier is comparable to other classifiers as it yields impressive results in multiple applications. An increase in the performance of the Naive Bayes Classifier is possible by identifying and forming segments of the data handled by the classifier. In this paper, a novel fuzzy-based fusion approach to selected quantitative features is proposed. The approach is used to improve the prediction accuracy of the Naive Bayes Classifier (NBC). The linguistic computing model with fusion operators, using ranked indexes of the linguistic terms in the dataset is made use in this proposed approach. Fuzzy values are generated only for the numerical attributes in the initial phase using 2-tuple linguistic computations. The equivalent real value computations are performed in order to express the results in the initial domain of the expression. These computations ensure improved comprehensiveness of the results of the classifier. The model incorporates the concepts of linguistic terms, fuzzy logic, fusion methods, and aggregation operations to the classical Naí¯ve Bayes Classifier. Such incorporation is used to improve the performance of the classifier in various decision-making applications. The proposed model is validated using a standard benchmark dataset-Stat log Heart disease dataset. It is obtained from the UCI Machine Learning Repository. The proposed Linguistic Fuzzy Naive Bayes Classifier showed better accuracy compared to the Simple Naive Bayes Classifier performance.

A Classification Framework using a Diverse Intensified Strawberry Optimized Neural Network (DISON) for Clinical Decision-making

A novel classification framework for clinical decision making that uses an Extremely Randomized Tree (ERT) based feature selection and a Diverse Intensified Strawberry Optimized Neural network (DISON) is proposed. DISON is a Feed Forward Artificial Neural Network where the optimization of weights and bias is done using a two phase training strategy. Two algorithms namely Strawberry Plant Optimization (SPO) algorithm and Gradient-descent Back-propagation algorithm are used sequentially to identify the optimum weights and bias. The novel two phase training method and the stochastic duplicate-elimination strategy of SPO helps in addressing the issue of local optima associated with conventional neural networks. The relevant attributes are selected based on the feature importance values computed using an ERT classifier.Vertebral Column, Pima Indian diabetes (PID), Cleveland Heart disease (CHD) and Statlog Heart disease (SHD) datasets from the University of California Irvine machine learning repository are used for experimentation. The framework has achieved an accuracy of 87.17% for Vertebral Column, 90.92% for PID, 93.67% for CHD and 94.5% for SHD. The classifier performance has been compared with existing works and is found to be competitive in terms of accuracy, sensitivity and specificity. Wilcoxon test confirms the statistical superiority of the proposed method.

Computer aided diagnostic system based on SVM and K harmonic mean based attribute weighting method

IntroductionMachine learning techniques are popular tool adopted for medical diagnosis and one of the core component of medical diagnostic system. The objective of machine learning techniques is to provide accurate and timely diagnostic results during disease diagnosis phases. Further, it also helps the physicians and medical practitioner regarding disease diagnosis. The objective of this work is to improve the diagnostic accuracy of computer aided diagnostic system. MethodLarge number of machine learning techniques are integrated in the computer aided diagnostic system for the prediction of the diseases. These machine learning techniques consider different features of disease to diagnosis the disease. It is seen that all features are not equally important in diagnostic process and irrelevant features can lead to low prediction rate. Hence in medical field, identification of irrelevant features is warm area of research. To identify the relevant features for disease prediction, attribute weighting methods are adopted. It is observed relevant features can improve the diagnostic accuracy of computer aided systems. Hence, to improve the diagnostic accuracy rate, a k harmonic mean based attribute weighting method is developed, called KhmAW. Further, the proposed KhmAW method is integrated with SVM method, called KhmAW-SVM. In KhmAW-SVM, KhmAW method is used to identify the relevant features from dataset and SVM method is applied for diagnosis the disease. The proposed method classifies the datasets into healthy and non-healthy classes. ResultsFour datasets are used to validate the proposed KhmAW-SVM based computer aided diagnostic system. These datasets are Statlog heart disease, Parkinson disease, Liver disease and Pima Indian diabetes disease datasets and having non linearly separable data distribution. The simulation results of proposed KhmAW-SVM method are evaluated using accuracy rate. Further, the simulation results are assessed using 50-50 training-testing and 10 fold methods. It is stated that proposed KhmAW-SVM method achieves 94.28%, 99%, 89.93% and 92.38% accuracy rates for heart disease, Parkinson's disease, liver disease and diabetes disease respectively. ConclusionThe efficacy of the proposed method is evaluated using four well known diseases datasets and compared with large number of existing studies. It is stated that proposed KhmAW-SVM based computer aided diagnostic system achieves better quality results as compared to existing studies. Hence, it is concluded that proposed computer aided diagnostic system can improve the clinical decision making process and also help the physician and doctors regarding different diseases.

Understanding Clinical Data using Exploratory Analysis

In today's world the data plays an indispensable role. The proper understanding of data and its interpretation lays the foundation for the growth and also the success of company or an organization. As in domains such as business, finance and banking, health sector also produces huge amounts of data. This data needs to be properly analyzed and summarized before the data is modeled for a specific purpose. Generally, clinical data involves stakeholders like doctors, technicians, lab analysts, hospital managers, care providers and insurance agents. Exploratory Data Analysis plays an important role in providing the complete picture of the dataset along with identifying new insights and hidden patterns in the data. As such it becomes the most significant step before actually preprocessing the data. In our paper we have implemented EDA on Statlog heart disease dataset to identify the important variables, correlations between any variables, missing values, outliers and PCA. To verify, whether the process of EDA actually impacts the performance we have utilized machine learning algorithms like Naïve Bayes, Logistic regression, Decision Tree, Support Vector Machine, Random forest. Results indicate that the performance of the prediction model considerably increases after performing EDA regardless of the type of prediction algorithm used. Also the analysis of the dataset with graphical results helps the stakeholders to make better decisions regarding their patients and their treatments. Understanding any clinical data before modeling would prevent erroneous models later and exploratory analysis helps in achieving it.

Feature Selection and Instance Selection from Clinical Datasets Using Co-operative Co-evolution and Classification Using Random Forest

ABSTRACT Co-operative co-evolution approach solves problems by breaking them into subproblems. The proposed framework for Clinical Decision Support System (CDSS) uses a co-operative coevolution approach which treats Feature Selection (FS) and Instance Selection (IS) as independent subproblems. FS and IS remove less relevant features and instances, respectively, thereby improving the overall performance of the system. In this work, both Feature and Instance selection are done using the wrapper approach, which uses co-operative co-evolution and random forest classifier. The reduced dataset is used to train a random forest classifier and this trained model helped in making clinical decisions. These decisions assist physicians as the second opinion for diagnosis and treatment. Wisconsin Diagnostic Breast Cancer (WDBC), Hepatitis, Pima Indian Diabetes (PID), Cleveland Heart Disease (CHD), Statlog Heart Disease (SHD), Vertebral Column, and Hepatocellular Carcinoma (HCC) datasets from the University of California Irvine (UCI) Machine Learning repository are used for experimentation. The proposed framework achieved an accuracy of 97.1%, 82.3%, 81.01%, 93.4%, 96.8%, 91.4%, and 72.2% for datasets WDBC, Hepatitis, PID, CHD, SHD, vertebral column, and HCC, respectively. The results prove that the CDSS-developed using co-operative coevolution can efficiently assist the physicians in decision-making.

A Data Classification Method in Machine Learning Based on Normalised Hamming Pseudo-Similarity of Fuzzy Parameterized Fuzzy Soft Matrices

In this study, we propose a classification method based on normalised Hamming pseudo-similarity of fuzzy parameterized fuzzy soft matrices (fpfs-matrices). We then compare the proposed method with Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy k-Nearest Neighbor (Fuzzy kNN) in terms of the performance criterions (accuracy, precision, recall, and F-measure) and running time by using four medical data sets in the UCI machine learning repository. The results show that the proposed method performs better than FSSC, FussCyier, HDFSSC, and Fuzzy kNN for “Breast Cancer Wisconsin (Diagnostic)”, “Immunotherapy”, “Pima Indian Diabetes”, and “Statlog Heart”.

Discretization for Naive Bayes taking the specifics of heart data into account

At the present time heart disease is a major cause of death. Factors such as physical inactiveness, obesity, diabetes, social isolation and aging are expected to make the situation worse. It is worsened even further with misdiagnosis of patients describing heart related issues. A probability decision support approach to diagnosis of heart disease based on Naive Bayes is discussed here as most hospitals collect patient records but these are rarely used for automatic decision support. The approach is analyzed on Statlog heart data with the focus on improving preprocessing methods. As the result, a discretization algorithm with Equal Frequency Discretization which considers the specifics of engaged heart disease patients is presented. Enhancements of achieved accuracy with the added discretization and in comparison with other machine learning algorithms are shown in experiments founded on 10-fold cross-validation.

A FAST CORRELATION FILTER BASED GRADIENT BOOSTING CLASSIFIER FOR DISEASE DIAGNOSIS

Disease diagnosis is the process to find the disease with specified details of a person's symptoms. Diagnosing the Disease is time consuming due to the need to analyze relevant microorganisms. Due to large growth in world’s population, Classification model receives a great deal in any domain of research and also a consistent tool for medical disease diagnosis. The domain of classification approach is used in the disease diagnosis, disease prediction, bio informatics and so on. However, an effective disease diagnosis model and the accuracy with the disease prediction were compromised. In order to obtain higher classification accuracy for heart and stroke disease diagnosis, a Fast Correlation Filter based Gradient Boosting Classifier (FCF GBC) technique is introduced. The main objective of the FCF-GBC technique is effectively performs disease diagnosis with two processing steps. Initially, Fast Correlation Filtering (FCF) algorithm is used to select the most relevant attributes (i.e. features) for disease diagnosis and filter out the irrelevant attributes in dataset. FCF uses symmetrical uncertainty to calculate the dependences of attributes and discovers the relevant attributes. After that, A Gradient Boosting Classifier is used for classifying and predicting the heart and stroke disease from the extracted attributes. Experimental evaluation is carried out using Statlog heart disease dataset and International Stroke Trial Database on the factors such as classification accuracy, classification time, error rate and true positive rate with respect to number of patients.

VALIDATION OF CLUSTERING METHODS FOR MEDICAL DATA SETS

Abstract Introduction: Data mining techniques have been increasingly applied to medical data in the past decade and are divided into two categories: predictive algorithms such as classification and descriptive algorithms such as clustering and association rule mining (ARM). Clustering means partitioning a data set into a set of clusters in such a way that the samples belonging to the same clusters are similar and those belonging to different clusters are dissimilar. These algorithms are currently used as a preprocessing technique prior to medical data mining and analyzing. However, clustering algorithms have different behaviors depending on the features of the data set. Therefore, in most applications, results of clustering are evaluated in terms of some validity clustering measures. In addition, most of the medical data sets are usually complex, with nonlinear patterns, which are extremely large and difficult to cluster. The aim of this study is to performance analyze several clustering techniques based on the basis of two different classes of clustering quality measures, named internal and stability, over the medical data sets. Methods: In this study, the performance of six common clustering algorithms, including hierarchal clustering, K-means, fuzzy C-means (FCM), self-organizing tree algorithm (SOTA), Diana and partitioning around medoids (PAM), has been examined by using four different medical data sets, which are available on UCI repositories, including the Indian liver patient data set (ILPD), Pima Indians diabetes, breast cancer, and statlog heart disease. The results of clustering methods have been evaluated based on two different classes of clustering quality measures called “internal” and “stability.” Stability measures include average proportion of non-overlap (APN), average distance (AD), average distance between means (ADM), and figure of merit (FOM) and internal measures consist of connectivity, Silhouette and Dunn index. Results: Due to given data sets consisting of two classes of samples, the number of cluster is assumed to two. The evaluation results showed that hierarchical clustering is the best algorithm to cluster all of the current data sets on the basis of internal validity measures. While based on stability measures, a Diana algorithm for ILPD and breast data sets, hierarchical clustering method for heart data set and SOTA for Pima data set outperformed other clustering algorithms. Conclusion: In this study, performance of several clustering techniques on the basis of internal and stability clustering measures over the medical data sets were investigated. The hierarchical clustering and Diana methods revealed better results. The number of clusters is assumed to two, while this parameter can have an effect on performance of different clustering algorithms. Future work will mainly cover the optimal estimation number of clusters and also consider more clustering methods for evaluation.

A Multi-objective Non-Dominated Sorted Artificial Bee Colony Feature Selection Algorithm for Medical Datasets

Objective: There exists a huge amount of heterogeneous data in medical databases which when mined may provide valuable information for medical diagnosis. The processing of this voluminous data is tedious, owing to its high dimensionality. The presence of irrelevant and redundant data may reduce the performance of data mining algorithms. The data preprocessing technique, feature selection, removes any irrelevant or redundant features and selects discriminative features. The objective of this research work is to propose a feature selection algorithm that meets two objectives, 1. Reduce the number of features and 2. Maximize the classification accuracy. Methods/Statistical Analysis: The research work proposes a feature selection algorithm Multi-Objective Non-Dominated Sorted Artificial Bee Colony Algorithm (NSABC) that combines Pareto optimization and Artificial Bee Colony (ABC) algorithm for selecting the Non-Dominant optimal feature subsets of three medical datasets viz. Wisconsin Breast Cancer, Pima Indian Diabetes and Statlog Heart disease datasets. The features selected by the ABC algorithm are further optimized by applying Pareto optimization. The feature subsets selected are validated by evaluating the performance of KNN classifier in terms of the classification metrics Precision, Recall and Accuracy before and after feature selection. Findings: The percentage of feature reduction and the improved performance of the classifier prove that the proposed feature selection method has selected the discriminate features of the datasets. The selected feature subsets are further validated by calculating the entropy, the measure signifying the individuality and independent nature of the features in the feature subset. Application/Improvement: The reduced feature subset selected is used in the easy diagnosis of the disease during the medical diagnosis. This also helps in cost and time reduction for the medical diagnosis of the disease.

Hybrid approach using fuzzy sets and extreme learning machine for classifying clinical datasets

Data mining techniques play a major role in developing computer aided diagnosis systems and expert systems that will aid a physician in clinical decision making. In this work, a classifier that combines the relative merits of fuzzy sets and extreme learning machine (FELM) for clinical datasets is proposed. The three major subsystems in the FELM framework are preprocessing subsystem, fuzzification subsystem and classification subsystem. Missing value imputation and outlier elimination are handled by the preprocessing subsystem. The fuzzification subsystem maps each feature to a fuzzy set and the classification subsystem uses extreme learning machine for classification. Cleveland heart disease (CHD), Statlog heart disease (SHD) and Pima Indian diabetes (PID) datasets from the University of California Irvine (UCI) machine learning repository have been used for experimentation. The CHD and SHD datasets have been experimented with two class labels one indicating the absence and the other indicating the presence of heart disease. The CHD dataset has also been experimented with five class labels, one class label indicating the absence of heart disease and the other four class labels indicating the severity of heart disease namely low risk, medium risk, high risk and serious. The PID data set has been experimented with two class labels one indicating the absence and the other indicating the presence of gestational diabetes. The classifier has achieved an accuracy of 93.55% for CHD data set with two class labels; 73.77% for CHD data set with five class labels; 94.44% for SHD data set and 92.54% for PID dataset.

Heart Diseases Diagnosis Using Neural Networks Arbitration

There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.