Smartphone-Based Heart Disease Classification Using Machine Learning Techniques
Abstract
Patients having heart diseases are diagnosed with a severe delay at times and further diagnosis in the absence of medical personnel can be fatal if the prediction is inaccurate. Therefore, this paper proposes the use of heart disease datasets to predict heart disease using various machine learning methods (Logistic Regression, Naive Bayes, Random Forest, k-nearest Neighbor, Support Vector Machine, Decision Tree Classifier, XGBoost Classifier, Artificial Neural Network). Cleveland, Hungarian, Switzerland, Long Beach VA and Statlog (Heart) datasets were used in this study which has 11 features of 1190 instances. The dataset was split into train and test sets with a ratio of 80:20. The performance was evaluated based on the accuracy, precision, recall, and F1 score for each of the models. From the eight models, the XGBoost Classifier outperformed other models with an accuracy of 93.7%. The trained model was integrated with the Android Studio framework to create the mobile application for the classification of heart disease.
References
Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2019). Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 36, 82–93. https://doi.org/10.1016/j.tele.2018.11.007
Begum, S., Siddique, F. A., & Tiwari, R. (2021). A Study for Predicting Heart Disease using Machine Learning. 12(10), 9.
Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. Computational Intelligence and Neuroscience, 2021, e8387680. https://doi.org/10.1155/2021/8387680
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and Their Applications, 13(4), 18–28. https://doi.org/10.1109/5254.708428
Jamtsho, Y., & Riyamongkol, P. (2019). VEHICLE NUMBER PLATE DETECTION AND RECOGNITION SYSTEM IN BHUTAN [Thesis, Naresuan University]. http://nuir.lib.nu.ac.th/dspace/handle/123456789/1468
Lakshmanarao, A., Srisaila, A., & Kiran, T. S. R. (2021). Heart Disease Prediction using Feature Selection and Ensemble Learning Techniques. 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 994–998. https://doi.org/10.1109/ICICV50876.2021.9388482
Navlani, A. (2018, December 28). Python Decision Tree Classification with Scikit-Learn DecisionTreeClassifier. DataCamp Community. https://www.datacamp.com/community/tutorials/decision-tree-classification-python
Padmaja, B., Srinidhi, C., Sindhu, K., Vanaja, K., Deepika, N. M., & Patro, E. K. R. (2021). Early and Accurate Prediction of Heart Disease Using Machine Learning Model. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), Article 6.
Raihan, M., Mondal, S., More, A., Boni, P. K., & Sagor, Md. O. F. (2017). Smartphone Based Heart Attack Risk Prediction System with Statistical Analysis and Data Mining Approaches. Advances in Science, Technology and Engineering Systems Journal, 2(3), 1815–1822. https://doi.org/10.25046/aj0203221
Siddhartha, M. (2020). Heart Disease Dataset (Comprehensive) [dataset]. IEEE. https://ieee-dataport.org/open-access/heart-disease-dataset-comprehensive
Subba, M. (2020). Cellular mobile subscriptions jump in second-quarter | Kuensel Online. Kuensel. https://kuenselonline.com/cellular-mobile-subscriptions-jump-in-second-quarter/
World Life Expectancy. (2018). Coronary Heart Disease in Bhutan. World Life Expectancy. https://www.worldlifeexpectancy.com/bhutan-coronary-heart-disease
