Machine learning (ML) is a powerful tool that unveils hidden insights from internet of things (IoT) data. These technologies enhance decision-making in education, security, business, and healthcare. In healthcare, they automate tasks such as maintaining records, predicting diagnoses, and monitoring patients in real time. However, different ML algorithms perform differently on various datasets, influencing results and clinical decisions. Understanding these ML algorithms and their application in handling IoT data in healthcare is crucial. This chapter highlights key ML algorithms for classification and prediction, providing an in-depth overview of their role in analyzing IoT medical data. The analysis reveals that different ML prediction algorithms have unique limitations, necessitating careful selection based on the dataset type for accurate healthcare predictions. The chapter also illustrates the use of IoT and ML in predicting future healthcare trends.
Top1. Introduction
Health prediction systems play a crucial role in healthcare by enabling hospitals to allocate outpatients to less crowded facilities efficiently. These systems increase the number of patients receiving the necessary medical attention and mitigate the often unpredictable fluctuations in patient flow within hospitals. Factors such as emergency incidents, including natural disasters or vehicle accidents, and the regular demand for outpatient services often drive the need for healthcare services (Mtonga et al., 2019). This unpredictable demand can strain hospitals that lack real-time patient flow data, while nearby facilities may need to be more utilized. Through the Internet of Things (IoT), we can bridge the gap between the virtual and physical world, facilitating efficient communication and instantaneous data collection via advanced microprocessor chips.
Grasping the fact that the primary aim of healthcare is to foster and sustain health by preventing and treating diseases is crucial. Equipment such as SPECT, PET, MRI, and CT scans are vital in identifying unseen irregularities or disturbances under the skin. These diagnostic tools are also effective in tracking specific ailments like epilepsy and heart attacks. The exponential population increase and the erratic expansion of chronic illnesses have put significant pressure on modern healthcare facilities, causing a surge in demand for resources such as medical staff and hospital beds. Consequently, an urgent requirement exists to alleviate the strain on healthcare systems while upholding service quality and standards.
The internet of things (IoT) presents promising avenues to lessen this stress, such as implementing RFID systems in medical establishments to cut down costs and enhance healthcare delivery. For instance, healthcare monitoring systems can proficiently track patients' cardiac activities, thereby assisting doctors in making precise diagnoses. Numerous wearable devices have been invented to guarantee consistent wireless data transmission. Yet, apprehensions about data security remain prevalent among IT and healthcare professionals, thus necessitating thorough research on amalgamating IoT with machine learning (ML) for patient monitoring while preserving data integrity.
The emergence of the Internet of Things (IoT) has signaled a transformative period in healthcare, empowering professionals to take a proactive approach toward patient engagement. The fusion of IoT with machine learning creates a platform capable of forecasting emergency care needs and devising suitable strategies. Regrettably, many outpatient departments grapple with overpopulated waiting areas. Patients visiting hospitals present a wide range of needs, with some requiring urgent medical assistance. This problem is magnified in developing nations where hospitals need adequate staff, often causing patients to leave without treatment due to the overwhelming crowd.
Many innovative ideas have been proposed as potential responses to these problems. For instance, a Wearable Medical Sensor (WMS) platform comprised of various applications and utilities was developed by Yuvaraj and SriPreethaa. They did substantial research into the application and development of WMSs, contrasting their efficiency with that of competing platforms. Patients experiencing cardiac arrest or Alzheimer's disease have significantly benefited from using these monitoring systems. Miotto et al.'s model, which relies on a WSN and a fuzzy logic network for monitoring, is also noteworthy. Micro-Electro-Mechanical Systems (MEMS) and a wireless sensor network (WSN) were combined to create a patient-centered Body Sensor Network (BSN) for continuous health monitoring. In critical situations, this system's ability to remotely monitor and transmit patient vitals to medical staff is invaluable.
Moreover, IoT applications have consistently monitored vital signs in patients with chronic illnesses. These systems utilize this information to project patients' health status. IoT sensors attached to the patient's body can discern their activity and predict potential health conditions. For example, the system can supervise diabetes patients to foresee disease progression and anomalies. This health prediction system can also recommend alternative hospitals for patients to seek treatment, reducing the risk of prolonged waiting periods or departure without treatment.
An example is Rajkomar et al.'s proposal of a Zigbee Technology-based BSN healthcare surveillance platform for remote patient monitoring. This platform can observe a broad spectrum of patient parameters and transmit them to a database through Wi-Fi or GPRS. Hence, merging IoT with machine learning has significantly enhanced patient care management by reinforcing the link between patients and doctors.