An Intelligent IoT-Driven Diagnostic Platform for Cardiovascular and Dermatological Disease Detection with Machine Learning
Abstract
The use of machine learning and IoT in diagnosing various diseases has improved through the provision of real-time and early diagnostic information. This paper will discuss a new, IoT-based, diagnostic system that could be used to diagnose both cardiovascular and dermatological disorders at the same time. The proposed platform leverages handcrafted features for the CVD diagnosis using deep neural networks in the ECG signals; as well as the real-time dermatological image analysis to improve the detection of skin cancer. Moreover, because of the adversarially robust model, the framework does not rely on perfect data and works well even in the conditions like noise or data loss. The adopted system design involves IoT devices with data acquisition, data processing, and data transmission to ensure round-the-clock patient monitoring and adequate diagnosis. Cardiovascular as well as dermatological domains also demonstrate the uses of ML and IoT on this platform to exhibit the fact that it would be capable of working on a number of healthcare fields. This paper aims to falling under the theme and realization of intelligent health monitoring systems through integrating latest technologies to aid in achieving a higher trajectory in diagnostics and thereby advancing patient care.
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N. Muthumani and K. Akilandeswari, "Optimized Feature Selection and Classification Framework for Cardiovascular Disease Using Statistical Normalization and Bio-Inspired Algorithms," 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS), Mathura, India, 2024, pp. 1-5, doi: 10.1109/CCIS63231.2024.10931965.
Y. Nikam and A. N. Paithane, "Advanced Comparative Analysis of Cardiovascular Heart Disease Detection Techniques: Insights from Empirical Results," 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE), Rourkela, India, 2024, pp. 1-6, doi: 10.1109/ICSPCRE62303.2024.10675120.
S. Venkata Kishore Babu and M. M. V. Chalapathi, "Advancing Heart Disease Diagnosis: Deep Stacked Ensemble Architecture Utilizing ECG Signal Image and Advanced Feature Sets," 2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Hamburg, Germany, 2024, pp. 107-112, doi: 10.1109/ICCCMLA63077.2024.10871605.
S. Diwan, N. Yadu, D. Singh, M. Chilamwar and M. Sahu, "Digitization and Classification of Electrocardiograms for Automated Cardiovascular Disease Diagnosis Using Machine Learning and Deep Learning Models," 2025 International Conference on Ambient Intelligence in Health Care (ICAIHC), Raipur Chattisgarh, India, 2025, pp. 1-6, doi: 10.1109/ICAIHC64101.2025.10956735.
Z. M. Tun and M. Aye Khine, "Cardiac Diagnosis Classification Using Deep Learning Pipeline on Apache Spark," 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Phuket, Thailand, 2020, pp. 743-746, doi: 10.1109/ECTI-CON49241.2020.9158314.
S. Pandya, T. R. Gadekallu, P. K. Reddy, W. Wang and M. Alazab, "InfusedHeart: A Novel Knowledge-Infused Learning Framework for Diagnosis of Cardiovascular Events," in IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 3060-3069, June 2024, doi: 10.1109/TCSS.2022.3151643.
N. Gadde, R. Kanaparti, S. Manukonda, A. Mohapatra and A. Venkatesh, "Automated Detection of Heart Disease Using PhysioNet ECG Signals and Machine Learning Models," 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), Hangzhou, China, 2024, pp. 352-358, doi: 10.1109/ICMLCA63499.2024.10754024.
N. Susitha, H. Selvi, N. B. Mahesh Kumar, V. Nagarajan, S. S. Ali and L. S. Kumar, "Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction," 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), Gobichettipalayam, India, 2023, pp. 496-500, doi: 10.1109/ICUIS60567.2023.00088.
V. Manimaran, N. Shanthi, N. Aravindhraj, K. M. Aatarsh, G. Adharshini and P. Gokul, "ECG - Driven Heart Disorder Profiler using Machine Learning Techniques," 2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC), Bengaluru, India, 2024, pp. 1-6, doi: 10.1109/ICECCC61767.2024.10593898.
F. Li, H. Chang, M. Jiang and Y. Su, "A Contrastive Learning Framework for ECG Anomaly Detection," 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, 2022, pp. 673-677, doi: 10.1109/ICSP54964.2022.9778634.
V. Manimaran, N. Shanthi, N. Aravindhraj, K. M. Aatarsh, G. Adharshini and P. Gokul, "Advancements in Heart Disease Classification: Leveraging Deep Learning Techniques for ECG Analysis," 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 2024, pp. 1-7, doi: 10.1109/ICCCNT61001.2024.10724585.
K. Rajmohan, I. Paramasivam and S. Sathyanarayan, "Prediction and Diagnosis of Cardio Vascular Disease -- A Critical Survey," 2014 World Congress on Computing and Communication Technologies, Trichirappalli, India, 2014, pp. 246-251, doi: 10.1109/WCCCT.2014.74.
S. Bhattacharyya, S. Majumder, P. Debnath and M. Chanda, "Arrhythmic Heartbeat Classification Using Ensemble of Random Forest and Support Vector Machine Algorithm," in IEEE Transactions on Artificial Intelligence, vol. 2, no. 3, pp. 260-268, June 2021, doi: 10.1109/TAI.2021.3083689.
K. Sathya and R. Karthiban, "Performance Analysis of Heart Disease Classification for Computer Diagnosis System," 2020 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2020, pp. 1-7, doi: 10.1109/ICCCI48352.2020.9104089.
Shah and D. Singh, "Cognitive Cardiovascular Disease Prediction Using ECG-Crown Auto Encoder Neural Network for ECG Time Series Signals," 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India, 2024, pp. 1-7, doi: 10.1109/ICCUBEA61740.2024.10774906.
M. Balipa, M. Murugappan and A. Castalino, "Artificial Intelligence based ECG Analysis to Assess Cardiac Health," 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL), Bhimdatta, Nepal, 2024, pp. 587-593, doi: 10.1109/ICSADL61749.2024.00101.
M. Hammad, A. M. Iliyasu, A. Subasi, E. S. L. Ho and A. A. A. El-Latif, "A Multitier Deep Learning Model for Arrhythmia Detection," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9, 2021, Art no. 2502809, doi: 10.1109/TIM.2020.3033072.
G. M., V. Ravi, S. V, G. E.A and S. K.P, "Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram," in IEEE Transactions on Engineering Management, vol. 70, no. 8, pp. 2787-2799, Aug. 2023, doi: 10.1109/TEM.2021.3104751.
G. Raja et al., "CardioNetFusion: A Deep Learning and Explainable AI Integrated System for Cardiovascular Disease Detection," 2024 IEEE International Conference on Data Mining Workshops (ICDMW), Abu Dhabi, United Arab Emirates, 2024, pp. 388-395, doi: 10.1109/ICDMW65004.2024.00056.
D. S. AbdElminaam, A. G. Fahmy, Y. M. Ali, O. A. D. El-Din, A. R. aly and M. Heidar, "DeepECG :Building an Efficient Framework for Automatic Arrhythmia classification model," 2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), Cairo, Egypt, 2022, pp. 203-209, doi: 10.1109/MIUCC55081.2022.9781646.
R. Banoth, A. K. Godishala, V. R and H. Yassin, "A Healthcare Monitoring System for Predicting Heart Disease through Recurrent Neural Network," 2022 IEEE 7th International conference for Convergence in Technology (I2CT), Mumbai, India, 2022, pp. 1-7, doi: 10.1109/I2CT54291.2022.9824888.
W. Liu et al., "Lead Separation and Combination: A Novel Unsupervised 12-Lead ECG Feature Learning Framework for Internet of Medical Things," in IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23897-23914, 1 Dec.1, 2022, doi: 10.1109/JIOT.2022.3188771.
A. Sinha, B. Kumar, P. Banerjee and M. Ramish, "HSCAD:Heart Sound Classification for Accurate Diagnosis using Machine Learning and MATLAB," 2021 International Conference on Computational Performance Evaluation (ComPE), Shillong, India, 2021, pp. 115-120, doi: 10.1109/ComPE53109.2021.9752199.
D. Gündüzalp and C. Zou, "Combined Self-Supervised Learning for ECG Classification Based on the Classification Token," 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Houston, TX, USA, 2024, pp. 1-8, doi: 10.1109/BHI62660.2024.10913542.
B. Ganguly, A. Ghosal, A. Das, D. Das, D. Chatterjee and D. Rakshit, "Automated Detection and Classification of Arrhythmia From ECG Signals Using Feature-Induced Long Short-Term Memory Network," in IEEE Sensors Letters, vol. 4, no. 8, pp. 1-4, Aug. 2020, Art no. 6001604, doi: 10.1109/LSENS.2020.3006756.
A. Oza et al., "A Novel Multimodal Framework for Early Detection of Congestive Heart Failure using Ensemble Learning based Fusion Approach," 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI), Prayagraj, India, 2024, pp. 1-6, doi: 10.1109/CVMI61877.2024.10781823.
L. Ibrahim, M. Mesinovic, K. -W. Yang and M. A. Eid, "Explainable Prediction of Acute Myocardial Infarction Using Machine Learning and Shapley Values," in IEEE Access, vol. 8, pp. 210410-210417, 2020, doi: 10.1109/ACCESS.2020.3040166.
M. T. Tahmid, M. E. Kader, T. Mahmud and S. A. Fattah, "MD-CardioNet: A Multi-Dimensional Deep Neural Network for Cardiovascular Disease Diagnosis From Electrocardiogram," in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 4, pp. 2005-2013, April 2024, doi: 10.1109/JBHI.2023.3308856.
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