Improving Heart Disease Prediction Using Feature Selection Approaches

Bashir, Saba; Khan, Zain Sikander; Khan, Farhan Hassan; Anjum, Aitzaz; Bashir, Khurram

doi:10.1109/ibcast.2019.8667106

Cited by 150 publications

(94 citation statements)

References 17 publications

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“…Different models such as Naïve Bayes, Random Forest and www.ijacsa.thesai.org other used in the experiment implemented using Rapid Miner tool. The output indicated the high accuracy measured due to feature selection approach [7]. Furthermore, the Extreme Learning Machine techniques using feedforward neural network applied on Cleveland data based on 300 patients, suggested 80% accuracy in forecasting the heart disease in a patient [8].…”

Section: Machine Learning Classifiers For Heart Disease Predictionmentioning

confidence: 96%

“…As previous studies suggested the common reasons behind heart disease can be unhealthy food, tobacco, excessive sugar, overweight or extra body fat [3], [6]. Whereas the common symptoms can be pain in arms and chest [7]. Noticeably, these reasons are independent from each other; proper analysis on this kind of dataset can improve the process of diagnosing and can assist the heart surgeons as well.…”

Section: Introductionmentioning

confidence: 95%

“…The results indicated that the support vector machine provided the optimal results between other implemented approaches. Bashir et al, (2019) attempted to improve the performance of heart disease prediction using feature selection approach. Different models such as Naïve Bayes, Random Forest and www.ijacsa.thesai.org other used in the experiment implemented using Rapid Miner tool.…”

Section: Machine Learning Classifiers For Heart Disease Predictionmentioning

confidence: 99%

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Implementation of Machine Learning Model to Predict Heart Failure Disease

Alotaibi¹

2019

IJACSA

175

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In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and surgeon's point of view, it is complex to predict the heart failure on right time. Fortunately, classification and predicting models are there, which can aid the medical field and can illustrates how to use the medical data in an efficient way. This paper aims to improve the HF prediction accuracy using UCI heart disease dataset. For this, multiple machine learning approaches used to understand the data and predict the HF chances in a medical database. Furthermore, the results and comparative study showed that, the current work improved the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical information systems would be useful to predict the HF or any other disease using the live data collected from patients.

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Section: Machine Learning Classifiers For Heart Disease Predictionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 95%

Section: Machine Learning Classifiers For Heart Disease Predictionmentioning

confidence: 99%

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Implementation of Machine Learning Model to Predict Heart Failure Disease

Alotaibi¹

2019

IJACSA

175

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“…As a result, improvement in this field is being hampered. Table I shows that almost all the past studies [5,6,7,10,12,14,15,16,8,19] were conducted on the secondary data [11] which was published by UCI (Machine Learning Repository) where the features were almost same. At the same time, this dataset is a little bit outdated too (donated in 1988).…”

Section: Introductionmentioning

confidence: 99%

“…Another downside of previous studies is less predictive accuracy (see Table I). A limited data set [5,6,7,14,18] as well as a lack of features extraction [5,6,13,14], are the main reasons behind this poor accuracy. On the other hand, a model has been [5] proposed where a multilayer perceptron neural network with backpropagation was used which gained 100% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Prediction based on External Factors: A Machine Learning Approach

Tamal¹,

Islam²,

Ahmmed³

et al. 2019

IJACSA

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Technology has immensely changed the world over the last decade. As a consequence, the life of the people is undergoing multiple changes that directly have positive and negative effects on health. Less physical activity and a lot of virtual involvements are pushing people into various healthrelated issues and heart disease is one of them. Currently, it has gained a great deal of attention among various life-threatening diseases. Heart disease can be detected or diagnosed by different medical tests by considering various internal factors. However, this type of approach is not only time-consuming but also expensive. Concurrently, there are very few studies conducted on heart disease prediction based on external factors. To bridge this gap, we proposed a heart disease prediction model based on the machine learning approach which enables predicting heart disease with 95% accuracy. To acquire the best result, 6 distinct machine learning classifiers (Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, Quadratic Discriminant, and Logistic Regression) were used. At the same time, sklearn.ensemble.ExtraTreesClassifier has been used to extract relevant features to improve predictive accuracy and control over-fitting. Findings reveal that Support Vector Machine (SVM) outperforms the others with greater accuracy (95%).

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