Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques

Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

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Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.

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An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection

Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.

An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier

Identification of significant features and data mining techniques in predicting heart disease

Corrigendum to "Identification of significant features and data mining techniques in predicting heart disease" [Telematics Informatics 36 (2019) 82-93]

https://iopscience.iop.org/article/10.1088/1742-6596/1339/1/012053/pdf

TweetGST: A Web Based Systems for Sentiment Analysis and Opinion Mining for GST in Malaysia

Electric two-wheelers are becoming increasingly popular across the world, particularly in cities where their small size and flexibility make them a viable option for navigating congested streets. One of the most challenging aspects of e-mobility on two-wheelers is precisely calculating their range. This might be an issue for riders who must go long distances or who have limited access to charging stations. Various factors can influence an electric two-wheeler range, making it challenging to predict how far it can travel on a single charge. To tackle this problem, most of the manufacturers offer range predictions based on both test data and real-world usage scenarios. However, these estimates are customized for specific vehicle models and testing parameters that may not apply in all circumstances. Additionally, it can be challenging to obtain comprehensive technical specifications for two-wheelers available in the market, as most manufacturers do not provide detailed technical information. Hence, it is crucial to address the challenge of range prediction for two-wheelers in general, which can be advantageous for riders. In this paper, we discuss the precise prediction of the remaining range of electric two-wheelers even without knowing detailed e-mobility technical information. An application is also developed only for this research purpose, which can provide navigation services. Our approach concentrates on user behavior, weather, road conditions, and the vehicle’s performance history, which is gathered through the application. The collected data are used to train the selected ML model on the cloud. We applied various machine learning algorithms before deploying in the cloud where the SVM algorithm demonstrated outstanding performance, with a mean absolute error of 150 m for an average distance of 7.46 km. Furthermore, the model’s performance was evaluated after deployment and tested having 130 m error on average.

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Mohammad Shafenoor Amin

Papers

Identification of significant features and data mining techniques in predicting heart disease

Corrigendum to "Identification of significant features and data mining techniques in predicting heart disease" [Telematics Informatics 36 (2019) 82-93]

TweetGST: A Web Based Systems for Sentiment Analysis and Opinion Mining for GST in Malaysia

An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques