scholarly journals Comparative Analysis for Heart Disease Prediction

2017 ◽  
Vol 1 (4-2) ◽  
pp. 227
Author(s):  
Sundas Naqeeb Khan ◽  
Nazri Mohd Nawi ◽  
Asim Shahzad ◽  
Arif Ullah ◽  
Muhammad Faheem Mushtaq ◽  
...  
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Efficient Prediction ◽  
Causes Of Deaths

Today, heart diseases have become one of the leading causes of deaths in nationwide. The best prevention for this disease is to have an early system that can predict the early symptoms which can save more life. Recently research in data mining had gained a lot of attention and had been used in different kind of applications including in medical. The use of data mining techniques can help researchers in predicting the probability of getting heart diseases among susceptible patients. Among prior studies, several researchers articulated their efforts for finding a best possible technique for heart disease prediction model. This study aims to draw a comparison among different algorithms used to predict heart diseases. The results of this paper will helps towards developing an understanding of the recent methodologies used for heart disease prediction models. This paper presents analysis results of significant data mining techniques that can be used in developing highly accurate and efficient prediction model which will help doctors in reducing the number of deaths cause by heart disease.

scholarly journals Ensemble model for Heart Disease Prediction

2021 ◽  
Vol 1 (4) ◽  
pp. 268-280
Author(s):  
Bamanga Mahmud , , , Ahmad ◽  
Ahmadu Asabe Sandra ◽  
Musa Yusuf Malgwi ◽  
Dahiru I. Sajoh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Ensemble Model ◽  
Prediction Probability

For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. Heart is one of the essential parts of human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical devices to support the systems and further to enhance the ability to diagnose and predict heart diseases. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researchers assessed that how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher developed the meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, as per the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has the high prediction probability score in the implementation of heart disease prediction.

scholarly journals A PROPOSED PARADIGM FOR INTELLIGENT HEART DISEASE PREDICTION SYSTEM USING DATA MINING TECHNIQUES

2021 ◽  
Vol 56 (4) ◽  
pp. 220-240
Author(s):  
Shimaa Ouf ◽  
Ahmed I. B. ElSeddawy
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Cross Validation ◽  
Prediction Models ◽  
Heart Diseases ◽  
Optimal Combination ◽  
Disease Prediction ◽  
Scientific Papers ◽  
Fold Cross Validation

The data mining techniques-based systems could have a crucial impact on the employees’ lifestyle to predict heart diseases. There are many scientific papers, which use the techniques of data mining to predict heart diseases. However, limited scientific papers have addressed the four cross-validation techniques of splitting the data set that plays an important role in selecting the best technique for predicting heart disease. It is important to choose the optimal combination between the cross-validation techniques and the data mining, classification techniques that can enhance the performance of the prediction models. This paper aims to apply the four-cross-validation techniques (holdout, k-fold cross-validation, stratified k fold cross-validation, and repeated random) with the eight data mining, classification techniques (Linear Discriminant Analysis, Logistic regression, Support Vector Model, KNN, Decision Tree, Naïve Bayes, Random Forest, and Neural Network) to improve the accuracy of heart disease prediction and select the best prediction models. It analyzes these techniques on a small and large dataset collected from different data sources like Kaggle and the UCI machine-learning repository. The evaluation metrics like accuracy, precision, recall, and F-measure were used to measure the performance of prediction models. Experimentation is performed on two datasets, and the results show that when the dataset is colossal (70000 records), the optimal combination that achieves the highest accuracy is holdout cross-validation with the neural network with an accuracy of 71.82%. At the same time, Repeated Random with Random Forest considers the optimal combination in a small dataset (303 records) with an accuracy of 89.01%. The best models will be recommended to the physicians in business organizations to help them predicting heart disease in employees into one of two categories, cardiac and non-cardiac, at an early stage. The early detection of heart diseases in employees will improve productivity in the business organization.

scholarly journals Predicting heart ailment in patients with varying number of features using data mining techniques

2019 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

scholarly journals Determination of Significant Features for Building an Efficient Heart Disease Prediction System

2019 ◽  
Vol 8 (2) ◽  
pp. 4499-4504
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

Heart disease prediction using data mining techniques

2017 ◽  
Author(s):  
Abhishek Rairikar ◽  
Vedant Kulkarni ◽  
Vikas Sabale ◽  
Harshavardhan Kale ◽  
Anuradha Lamgunde
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques

2020 ◽  
Vol 12 (23) ◽  
pp. 9790
Author(s):  
Sanghoon Lee ◽  
Keunho Choi ◽  
Donghee Yoo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Prediction Model ◽  
Internal Control ◽  
Prediction Models ◽  
Influential Factors ◽  
Financial Information ◽  
Data Mining Techniques ◽  
Feasibility Assessment ◽  
Using Data

The government makes great efforts to maintain the soundness of policy funds raised by the national budget and lent to corporate. In general, previous research on the prediction of company insolvency has dealt with large and listed companies using financial information with conventional statistical techniques. However, small- and medium-sized enterprises (SMEs) do not have to undergo mandatory external audits, and the quality of accounting information is low due to weak internal control. To overcome this problem, we developed an insolvency prediction model for SMEs using data mining techniques and technological feasibility assessment information as non-financial information. We divided the dataset into two types of data based on three years of corporate age. The synthetic minority over-sampling technique (SMOTE) was used to solve the data imbalance that occurred at this time. Six insolvency prediction models were created using logistic regression, a decision tree, an artificial neural network, and an ensemble (i.e., boosting) of each algorithm. By applying a boosted decision tree, the best accuracies of 69.1% and 82.7% were derived, and by applying a decision tree, nine and seven influential factors affected the insolvency of SMEs established for fewer than three years and more than three years, respectively. In addition, we derived several insolvency rules for the two types of SMEs from the decision tree-based prediction model and proposed ways to enhance the health of loans given to potentially insolvent companies using these derived rules. The results of this study show that it is possible to predict SMEs’ insolvency using data mining techniques with technological feasibility assessment information and find meaningful rules related to insolvency.

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