Heart disease is a rapidly increasing disease that causes death worldwide. Therefore, scientists around the globe start studying this issue from a different perspective to assure early prediction of diagnosis to save patients' life from bad consequences that cause death. In this regard, Internet of Medical Things (IoMT) applications and algorithms should be utilized effectively to overcome this problem. Hoeffding Tree Algorithm (HTA) is a standard decision tree algorithm to handle large sizes of data sets. In this paper, an Adaptive Hoeffding Tree (AHT) algorithm is suggested to carry out classifications of data sets for early diagnosis of heart disease-related factors, and the obtained results by this algorithm are compared with other suggested Machine Learning (ML) algorithms in the literature. Therefore, a total of 3000 records of data sets are used in the classification, 33% of the data are utilized for female patient information, and the rest of the data are utilized for male patient information. In the original data set, each patient record includes 76 attributes, however only the most important 16 patient attributes are used for the classification. Data are retrieved from the University of California Irvine (UCI) Machine Learning Repository, which is collected from the Hungarian Institute of Cardiology, University Hospital at Zurich, University Hospital at Basel, and V.A. Medical Center. The obtained results from this study and the provided comparative results show the effectiveness of the AHT algorithm over other ML algorithms. Compared to other ML algorithms, AHT outperforms other algorithms with 95.67% accuracy for early estimation of diagnosis of heart disease.

[1] Adhikari U., Morris T., and Pan S., “Applying Hoeffding Adaptive Trees for Real-Time Cyber- Power Event and Intrusion Classification,” IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 4049-4060, 2018.

[2] Aljanabi M. and Ismail M., “Improved Intrusion Detection Algorithm based on TLBO and GA Algorithms,” The International Arab Journal of Information Technology, vol. 18, no. 2, pp. 170- 179, 2021.

[3] Alkhodari M., Jelinek H., Werghi N., Hadjileontiadis L., and Khandoker A., “Investigating Circadian Heart Rate Variability in Coronary Artery Disease Patients with Various Degrees of Left Ventricle Ejection Fraction,” in Proceedings 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Montreal, pp. 714-717, 2020.

[4] Al-Mahmud O., Khan K., Roy R., and Mashuque A., “Internet of Things (IoT) Based Smart Health Care Medical Box for Elderly People,” in Proceedings International Conference for Emerging Technology, Belgaum, pp. 1-6, 2020.

[5] Akkaya B., Sener E., and Gursu C., “A Comparative Study of Heart Disease Prediction Using Machine Learning Techniques,” in Proceedings International Congress on Human- Computer Interaction, Optimization and Robotic Applications, Ankara, pp. 1-8, 2022.

[6] Antony A. and Varghese K., “High Throughput Hardware for Hoeffding Tree Algorithm with Adaptive Naive Bayes Predictor,” in Proceedings 6th International Conference for Convergence in Technology, Maharashtra, pp. 1-6, 2021.

[7] Blake C. and Merz C., UCI Repository of Machine Learning Databases, 1998.

[8] Cao R., Tang Z., Liu C., and Veeravalli B., “A Scalable Multi-Cloud Storage Architecture for Cloud-Supported Medical Internet of Things,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1641-1654, 2020.

[9] Chen G., Ding C., Li Y., Hu X., Li X., Ren L., Ding X., Tian P., and Xue W., “Prediction of Chronic Kidney Disease Using Adaptive Hybridized Deep Convolutional Neural Network on the Internet of Medical Things Platform,” IEEE Access, vol. 8, 2020.

[10] Cihan P. and Coşkun H., “Performance Comparison of Machine Learning Models for Diabetes Prediction,” in Proceedings of Signal Processing and Communications Applications Conference, Istanbul, pp. 1-4, 2021.

[11] Dhanka S. and Maini S., “Random Forest for Heart Disease Detection: A Classification Approach,” in Proceedings of 2nd International Conference on Electrical Power and Energy Systems, Bhopal, pp. 1-3, 2021.

[12] Durga S., Nag R., and Daniel E., “Survey on Machine Learning and Deep Learning Algorithms used in Internet of Things (IoT) Healthcare,” in Proceedings of International Conference on Computing Methodologies and Communication (ICCMC), Erode, 2019.

[13] El-Shafiey M., Hagag A., El-Dahshan E., and Ismail M., “Heart-Disease Prediction Method Using Random Forest and Genetic Algorithms,” in Proceedings of International Conference on Electronic Engineering, Menouf, pp. 1-6, 2021.

[14] Elbasi E. and Zreikat A., “Efficient Early Prediction and Diagnosis of Diseases Using Machine Learning Algorithms for IoMT Data,” IEEE World AI IoT Congress, Seattle, pp. 0155- 0159, 2021.

[15] Elbasi E., Ayanoglu D., and Zreikat A., “Determination of Patient's Hearing Sensitivity using Data Mining Techniques,” in Proceedings of IEEE 12th International Conference on Application of Information and Communication Technologies, Almaty, pp. 1-6, 2018.

[16] Elbasi E., “Reliable Abnormal Event Detection from Iot Surveillance Systems,” in Proceedings of 7th International Conference on Internet of Things: Systems, Management, and Security, Paris, pp. 1-5, 2020.

[17] Elbasi E., Topcu A., and Mathew S., “Prediction of COVID-19 Risk in Public Areas Using IoT and Machine Learning,” Electronics, vol. 10, no. 14, 2021.

[18] Elbasi E., “B-DCT based Watermarking Algorithm for Patient Data Protection in IoMT,” in Proceedings of International Conference on Information Security and Cryptology, Ankara, pp. 1-4, 2020.

[19] Fazakis N., Kocsis O., Dritsas E., Alexiou S., Fakotakis N., and Moustakas K., “Machine Learning Tools for Long-term Type 2 Diabetes Risk Prediction,” IEEE Access, 2021.

[20] García-Martín E., Lavesson N., Grahn H., Casalicchio E. and Boeva V., “Hoeffding Trees with Nmin Adaptation, ” in Proceedings of IEEE 5th International Conference on Data Science and Advanced Analytics, Turin, pp. 70-79, 2018.

[21] Gatouillat A., Badr Y., Massot B. and Sejdić E., “Internet of Medical Things: A Review of Recent Contributions Dealing with Cyber-Physical Systems in Medicine,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3810-3822, 2018.

[22] Gopikrishnan S., Priakanth P., Srivastava G., and Fortino G., “EWPS: Emergency data communication on the Internet of Medical Things,” IEEE Internet of Things Journal, 2021.

[23] Han M., Ding J., and Li J., “PatHT: An Efficient Method of Classification over Evolving Data Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding ... 47 Streams,” The International Arab Journal of Information Technology, vol. 16, no. 6, pp. 1098 1105, 2020.

[24] Hulten G., Spencer L., and Domingos P., “Mining Time-Changing Data Streams,” in Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California, pp. 97-106, 2021.

[25] Jayakrishnan A. Visakh R., and Ratheesh T., “Computational Approach for Heart Disease Prediction using Machine Learning,” in Proceedings of International Conference on Communication, Control and Information Sciences, Idukki, pp. 1-5, 2021.

[26] Jia N. and Zheng C., “Design of Intelligent Medical Interactive System Based on Internet of Things and Cloud Platform,” in Proceedings of International Conference on Intelligent Human- Machine Systems and Cybernetics, Hangzhou, pp. 28-31, 2018.

[27] Junaid M. and Kumar R., “Data Science and Its Application in Heart Disease Prediction,” in Proceedings of International Conference on Intelligent Engineering and Management, London, pp. 396-400, 2020.

[28] Kavitha M., Gnaneswar G., Dinesh R., Sai Y., and Suraj R., “Heart Disease Prediction using Hybrid Machine Learning Model,” in Proceedings of 6th International Conference on Inventive Computation Technologies, Coimbatore, pp. 1329-1333, 2021.

[29] Khan M. and Algarni F., “A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS,” IEEE Access, vol. 8, 2020.

[30] Khan M. and Mondal M., “Effectiveness of Data- Driven Diagnosis of Heart Disease,” in Proceedings of International Conference on Electrical and Computer Engineering, Dhaka, pp. 419-422, 2020.

[31] Laha S., Ghosh D., and Swarnakar B., Internet of Medical Things for Smart Healthcare, Springer Singapore, 2020.

[32] Lakshmanarao A., Srisaila A., and Kiran T., “Heart Disease Prediction using Feature Selection and Ensemble Learning Techniques,” in Proceedings of 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, Tirunelveli, pp. 994- 998, 2021.

[33] Lu S. Wang A., Jing S., Shan T., Zhang X., Guo Y., and Liu Y., “A Study on Service-Oriented Smart Medical Systems Combined with Key Algorithms in The Iot Environment,” China Communications, vol. 16, no. 9, pp. 235-249, 2019.

[34] Mrabet Z., Selvaraj D., and Ranganathan P., “Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets,” in Proceedings of IEEE International Conference on Big Data (Big Data), Los Angeles, pp. 5697-5704, 2019.

[35] Nayak S., Gourisaria M., Pandey M., and Rautaray S., “Prediction of Heart Disease by Mining Frequent Items and Classification Techniques,” in Proceedings of International Conference on Intelligent Computing and Control Systems, Madurai, pp. 607-611, 2019.

[36] Rahman B., Hendric S., Sabarguna B., and Budiharto W., “Heart Disease Classification Model Using K-Nearest Neighbor Algorithm,” in Proceedings of 6th International Conference on Informatics and Computing, Jakarta, pp. 1-4, 2021.

[37] Rathnayake R., Karunarathne M., Nafi N., and Gregory M., “Cloud-Enabled Solution for Privacy Concerns in the Internet of Medical Things,” in Proceedings of International Telecommunication Networks and Applications Conference, Sydney, pp. 1-4, 2018.

[38] Rauscher J. and Bauer B., “Safety and Security Architecture Analyses Framework for the Internet of Things of Medical Devices,” in Proceedings of International Conference on e-Health Networking, Applications and Services, Ostrava, pp. 1-3, 2018.

[39] Satu M., Tasnim F., Akter T., and Halder S., “Exploring Significant Heart Disease Factors based on Semi-Supervised Learning Algorithms,” in Proceedings of International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, Rajshahi, pp. 1-4, 2018.