Heart Disease Analysis of Smart Healthcare System using Data Mining and Machine Learning
Keywords:
Heart Disease Analysis, Smart Healthcare System, Data Mining, Machine Learning, Disease Prediction, Clinical Decision Support SystemAbstract
Heart disease is one of the leading causes of mortality worldwide, highlighting the importance of early diagnosis and continuous health monitoring. The rapid growth of smart healthcare systems, supported by electronic health records, wearable devices, and Internet of Things (IoT) technologies, has resulted in the generation of large volumes of medical data. Efficient analysis of this data is essential for timely and accurate disease prediction. This research presents an intelligent heart disease analysis framework for smart healthcare systems using data mining and machine learning techniques. Clinical attributes such as age, gender, blood pressure, cholesterol level, blood sugar, heart rate, and electrocardiographic results are analyzed to predict the presence of heart disease. Various machine learning classifiers are applied and evaluated to identify the most effective prediction model. Experimental results demonstrate that machine learning-based approaches significantly improve prediction accuracy and reliability compared to traditional diagnostic methods. The proposed system supports early detection, enhances clinical decision-making, and contributes to improved patient care within smart healthcare environments.
References
H. El-Sofany, B. Bouallegue, and Y. M. Abd El-Latif, “A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method,” Scientific Reports, vol. 14, pp. 1–18, 2024.
C. M. Bhatt, P. Patel, T. Ghetia, and P. L. Mazzeo, “Effective heart disease prediction using machine learning techniques,” Journal of Medical Systems, vol. 16, p. 88, 2023.
O. Taylan, A. Alkabaa, H. Alqabbaa, E. Pamukçu and V. Leiva, "Early prediction in classification of cardiovascular diseases with machine learning neuro-fuzzy and statistical methods", Biology, vol. 12, no. 1, pp. 117, 2023.
I. Sutedja, "Descriptive and predictive analysis on heart disease with machine learning and deep learning", 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS), pp. 1-6, 2021, October.
Karna Vishnu Vardhana Reddy, Irraivan Elamvazuthi, Azrina Abd Aziz, Sivajothi Paramasivam and Hui Na Chua, “Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis”, International Conference on Intelligent and Advanced Systems (ICIAS), pp. 01-05, IEEE 2021.
M. Ganesan and Dr. N. Sivakumar, “IoT based heart disease prediction and diagnosis model for healthcare using machine learning models”, International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 01-05, IEEE 2019.
Priyan Malarvizhi Kumar, Usha Devi Gandhi, “A novel Internet of Things architecture with machine learning algorithm for early detection of heart diseases", Computers and Electrical Engineering, Vol.65, pp. 222–235, 2018.
Prabal Verma, Sandeep K. Sood, "Cloud-centric IoT based disease diagnosis healthcare framework", Journal of Parallel Distribution Computer, Vol. 116, Issue 06, pp. 27-38, 2018.
Amin Khatami and Abbas Khosravi, “Medical image analysis using wavelet transform and deep belief networks”, Journal of Expert Systems with Applications, Vol. 3, Issue 4, pp. 190–198, 2017.
Sanjay Kumar Sen, "Predicting and Diagnosing of Heart Disease using Machine Learning Algorithms", International Journal of Engineering and Computer Science (IJECS), Vol. 6, Issue 7, pp. 21623-21631, 2017.
X Liu, X Wang, Q Su M Zhang and Y Zhu Q Wang, “A hybrid classification system for heart disease diagnosis based on the RFRS method", Computational and mathematical methods in medicine, pp. 01-11, 2017.
Ashwini Shetty, Naik, C., “Different data mining approaches for predicting heart disease”, International journal of innovative research in science, engineering and technology, Vol. 3, Issue 2, pp. 277–281, 2016.
Aydin, S., “Comparison and evaluation data mining techniques in the diagnosis of heart disease”, Indian journal of science and technology, Vol. 6, Issue 1, pp. 420–423, 2016.
Bayasi, N. and Tekeste, “Low-power ECG-based processor for predicting ventricular arrhythmia”, Journal of IEEE transactions on very large scale integration systems, Vol. 24, Issue 5, pp. 1962–1974, 2016.
Berikol, B. and Yildiz, “Diagnosis of acute coronary syndrome with a support vector machine”, Journal of Medical System, Vol. 40, Issue 4, pp. 11–18, 2016.
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