scholarly journals Classification and Feature Selection Approaches by Machine Learning Techniques: Heart Disease Prediction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
N. Satish Chandra Reddy ◽  
Song Shue Nee ◽  
Lim Zhi Min ◽  
Chew Xin Ying
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Causes Of Death ◽  
Disease Diagnosis ◽  
Performance Model ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Heart Disease Diagnosis ◽  
The Cost

The heart disease has been one of the major causes of death worldwide. The heart disease diagnosis has been expensive nowadays, thus it is necessary to predict the risk of getting heart disease with selected features. The feature selection methods could be used as valuable techniques to reduce the cost of diagnosis by selecting the important attributes. The objectives of this study are to predict the classification model, and to know which selected features play a key role in the prediction of heart disease by using Cleveland and statlog project heart datasets. The accuracy of random forest algorithm both in classification and feature selection model has been observed to be 90–95% based on three different percentage splits. The 8 and 6 selected features seem to be the minimum feature requirements to build a better performance model. Whereby, further dropping of the 8 or 6 selected features may not lead to better performance for the prediction model.

scholarly journals Review on Machine Learning Techniques for Diagnosis of Heart Disease

2021 ◽  
Vol 9 (8) ◽  
pp. 1418-1420
Author(s):  
Harshal P. Sabale
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Human Health ◽  
World Health Organisation ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Heart Disease Diagnosis

Abstract: Now-a-days, heart disease is becoming a concern to human health. According to World Health organisation (WHO), heart disease is the number one killer among other fatal diseases. Excessive smoking, alcohol consumption and junk food are culprit for the heart disease. Physical inactivity is also a concerning to the human health. Heart disease is pretty hard to predict or diagnose using traditional methods like counselling. But, now-a-days, medical fields are using machine learning to predict or diagnose different diseases. Implementation of machine learning techniques provides faster and mostly accurate results. This can save many life. In this paper, different machine learning approach for heart disease diagnosis are reviewed. Keywords: Heart disease, CVD, Machine Learning

scholarly journals Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Mohammad Alsaffar ◽  
Abdullah Alshammari ◽  
Gharbi Alshammari ◽  
Saud Aljaloud ◽  
Tariq S. Almurayziq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Disease Diagnosis ◽  
Ischemic Heart ◽  
Machine Learning Techniques ◽  
Medical Community ◽  
Electrocardiogram Signals ◽  
Learning Techniques ◽  
Heart Disease Diagnosis

Heart disease is the leading cause of death from chronic diseases in the developing countries. The difficulty of making an accurate and timely diagnosis is exacerbated by a lack of resources and professionals in some areas, which contributes to this reality. Medical professionals may benefit from technological advancements that aid in the accurate diagnosis of patients. In light of these findings, a hybrid diagnostic tool has been developed that combines several computational intelligence (machine learning) techniques capable of analyzing clinical histories and images of electrocardiogram signals and indicating whether or not the patient has ischemic heart disease with up to 97.01% accuracy. Working with medical experts and a database containing clinical data on approximately 1020 patients and their diagnoses was required for this project. Both were put to use. A picture database containing 92 images of electrocardiogram signals was also used in this project for the analysis of the Artificial Neural Network. After extensive research and testing by the medical community, which supported the project and provided positive feedback, a successful tool was developed. This demonstrated the tool’s effectiveness.

scholarly journals Heart disease prediction using SVM based neuro-fuzzy technique in the cloud computing

2018 ◽  
Vol 7 (2.20) ◽  
pp. 153
Author(s):  
Dr M. Sadish Sendil
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Feature Selection ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Disease Prediction ◽  
Specific Level ◽  
Huge Data ◽  
Neuro Fuzzy ◽  
Heart Disease Diagnosis

Cloud computing is a technique for conveying on information development benefits in resources are recovered from the Internet through online based device and applications, as opposed to a speedy association with a server. Cloud has numerous applications in the meadows of education, social networking, and medicine. But the benefit of the cloud for medical reasons is seamless, specifically an account of the huge data generated by the healthcare industry. Heart disease diagnosis determination strategy is essential and significant issue for the patient's wellbeing. Furthermore, it will help to decrease infection to a more specific level. Computer-aided decision support method performs a vital task in medical line. Data mining gives the system and innovation to change these heaps of data into effective information for decision-making. When applying data mining techniques it carries shorter time for the prediction of the disease with more exactness. The hybrid work of preprocessing, feature selection using SVM and SVM based Neuro-Fuzzy data mining strategies utilizing as a part of the determination of the heart disease is incredibly impressive. The framework is to build up a technique for arranging for heart level of the patient relies upon highlight information utilizing Neuro-Fuzzy surmising system. The experiment is done with two different analysis that is one with preprocessed data alone and applied SVM based Neuro Fuzzy Technique and the second one is accomplished with feature selection done data and applied SVM based Neuro Fuzzy Technique. The results prove that the system result of the first one gives 92% accuracy in the heart disease prediction. The second one is giving 95.11% accuracy in the heart disease prediction.  

scholarly journals Modelling an Effectual Glowworm Swarm Optimization Strategy for Feature Selection in Heart Disease Prediction

2020 ◽  
Vol 9 (1) ◽  
pp. 500-505
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Common Disease ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Heart Disease Diagnosis ◽  
Sensitivity Specificity

Heart disease is measured as a common disease all over the world. The ultimate target is to provide heart disease diagnosis with improved feature selection with Glow worm swarm optimization algorithm. The anticipated model comprises of optimization approach for feature selection and classifier for predicting heart disease. This system framework comprises of three stages: 1) data processing, 2) feature selection using IGWSO approach and classification with conventional machine learning classifiers. Here, C4.5 classifier is considered for performing the function. The benchmark dataset that has been attained from UCI database was cast off for performing computation. Maximal classification accuracy has been achieved based on cross validation strategy. Outcomes depicts that performance of anticipated model is superior in contrary to other models. Simulation has been done with MATLAB environment. Metrics like accuracy, sensitivity, specificity, F-measure and recall has been evaluated

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 Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis

2020 ◽  
Vol 10 (18) ◽  
pp. 6626 ◽  
Author(s):  
Afnan M. Alhassan ◽  
Wan Mohd Nazmee Wan Zainon
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Selection Process ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Deep Belief Network ◽  
Belief Network ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm ◽  
Heart Disease Diagnosis

Contemporary medicine depends on a huge amount of information contained in medical databases. Thus, the extraction of valuable knowledge, and making scientific decisions for the treatment of disease, has progressively become necessary to attain effective diagnosis. The obtainability of a large amount of medical data leads to the requirement of effective data analysis tools for extracting constructive knowledge. This paper proposes a novel method for heart disease diagnosis. Here, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection, and can be utilized for handling high dimensional data. Then, the selected features are given to the deep belief network (DBN), which is trained using the proposed Taylor-based bird swarm algorithm (Taylor-BSA) for detection. Here, the proposed Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA). The proposed Taylor-BSA–DBN outperformed other methods, with maximal accuracy of 93.4%, maximal sensitivity of 95%, and maximal specificity of 90.3%, respectively.

scholarly journals New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

2019 ◽  
Vol 10 (3) ◽  
pp. 667-678 ◽  
Author(s):  
Jalil Nourmohammadi-Khiarak ◽  
Mohammad-Reza Feizi-Derakhshi ◽  
Khadijeh Behrouzi ◽  
Samaneh Mazaheri ◽  
Yashar Zamani-Harghalani ◽  
...  
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Nearest Neighbor ◽  
Imperialist Competitive Algorithm ◽  
Disease Diagnosis ◽  
K Nearest Neighbor ◽  
Feature Selection Technique ◽  
Medical Databases ◽  
Classification Evaluation ◽  
Heart Disease Diagnosis

AbstractThe number and size of medical databases are rapidly increasing, and the advanced models of data mining techniques could help physicians to make efficient and applicable decisions. The challenges of heart disease data include the feature selection, the number of the samples; imbalance of the samples, lack of magnitude for some features, etc. This study mainly focuses on the feature selection improvement and decreasing the numbers of the features. In this study, imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease. This algorithm can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms. Also, the K-nearest neighbor algorithm is used for the classification. Evaluation result shows that by using the proposed algorithm, the accuracy of feature selection technique has been improved.

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