# Embedded AI-Based Digi-Healthcare
## 2 Related Work  
Cardiovascular diseases (CVD) represent the major causes of death in Asia and Europe, the mortality rate is higher among women and patients with a low socioeconomic status. Therefore, there is an urgent need for a solution that will periodically monitor patients' vital parameters and provide treatment plans based on medical data and health status. In this section, different surveyed papers are discussed to assess the performance of digital healthcare systems already developed and how they contribute to healthcare IoT. One paper [4] highlights the importance of healthcare monitoring systems for cardiovascular disease and the need of sharing the bioinformatics of patients living in rural areas with medical professionals. This is extremely important since residents of far-off areas are alienated from the urban population or face scarcity of medical resources. This paper discusses the implementation and design of a wearable electrocardiogram (ECG) monitor, interfaced with Arduino. The system architecture includes bio-sensing modules, a cloud (Google firebase) to store the real-time data of patients, and the web interface for medical professionals to view the data and offer timely diagnosis and treatment of any incurring cardiovascular disease. The major outcome of this study is the design of an IoT-based, low-cost, portable, and complete real-time ECG acquisition, transmission, storage, and visualization system. This real-time data can be visualized by medical professionals for offering a diagnosis of cardiac diseases. In addition to that, the data is also fed to a diagnostic application with a graphical user interface (GUI). The main drawback of this system is that there may be latency and network connectivity issues, especially in rural areas. To address this critical problem, our system has an edge-based architecture that performs local anomaly detection based on ML to deal with emergencies. A similar approach has been taken in [5] in which the authors highlight the importance of ECG signals of cardiovascular patients. They present an IoT-based health monitoring system that uses a statistical model, the Hidden Markov Model, to observe the underlying characteristics of ECG signals recorded via a sensor. This scheme aims to facilitate improved monitoring and timely intervention for CVD patients thereby enhancing medical services for such patients. The system implementation employs a patient path estimator, patient table, and alert management schemes within the hospital to facilitate the localization and timely intervention for the treatment of CVD patients. ECG signal is classified based on amplitude R peaks, and the time between each RR peak. These altogether are used for the detection of common and important rhythm disorder peaks. Based on comparison data of 50 normal patients and 50 CVD patients, the thresholds of \(\Delta\)RR \(>\) 150 ms and t period \(>\)60 ms are set, resultantly offering a high positive prediction of CVD. The drawback is that this paper lacks the implementation of smart technology powered by AI and ML. Hence, many improvements are still needed for refining the accuracy of the diagnostic model. In [2], the researchers are of the view that the evolution in ML technologies has made it possible for early disease detection and diagnosis. This is especially useful in cases of chronic illnesses, particularly heart diseases. This paper proposes a custom machine learning algorithm for the prediction of the possibility of heart disease in the next ten years. They have named their algorithm as Hard Voting (HV) classifier. HV has been made with the well-known classifiers: Logistic Regression, Random Forest, Multilayer Perceptron, and Gaussian Naive Bayes classifiers. The meta-classifier takes multiple risk factors (e.g., age, sex, smoking, systolic blood pressure, etc.) as inputs. The four classifiers are used, and the best prediction is considered by taking a vote. The authors used a publicly available dataset on Kaggle which comprises 3751 patients' data.