Artificial Intelligence Applications in Predictive Healthcare Systems

One industry that has been hit particularly hard by the technological revolution of the 21st century is healthcare. The use of AI in healthcare systems, especially in the field of predictive healthcare, is one of the most ground-breaking innovations of the last few years. The goal of predictive healthcare systems is to enhance healthcare efficiency by identifying and addressing any health concerns before they become completely apparent. This will lead to better management of resources, better patient outcomes, and early intervention. The objectives of predictive healthcare may be greatly advanced with the use of artificial intelligence (AI), thanks to its capacity to analyse large datasets, spot trends, and gain knowledge through experience. These smart systems collect information from many sources, including social and environmental variables, genetic databases, wearable health devices, and electronic health records (EHRs). This allows them to draw conclusions that were previously unavailable using conventional approaches. Predictive models powered by AI can do things like identify people at high risk for chronic diseases, suggest individualised treatment approaches, optimise clinical decision-making, and anticipate the likelihood of disease outbreaks. Some examples of diseases that have been successfully predicted by machine learning algorithms include diabetes, cardiovascular disease, Alzheimer's disease, and certain forms of cancer. The system is able to identify risk signs and minor connections that human practitioners would miss (Yousef Shaheen, 2021) because algorithms are trained on previous patient data, which includes symptoms, test findings, genetic markers, and lifestyle variables.

Artificial intelligence (AI) has the ability to revolutionise predictive healthcare in many ways, including but not limited to diagnosis, prevention, and population health management. Artificial intelligence systems may analyse patterns in real-time data to aid in the early detection of epidemics, evaluation of public health risks, and guidance of community and individual-level treatments. During the COVID-19 pandemic, these skills were put to the test. Artificial intelligence technologies were important in modelling infection trends, identifying individuals at risk, and optimising the delivery of vaccines. In addition, artificial intelligence (AI) improves the performance of mobile and wearable health devices by encouraging proactive patient behaviour and prompt medical consultations through the provision of real-time alerts and feedback based on physiological data. These devices keep a constant eye on your vitals including blood pressure, glucose levels, and heart rate. They utilise artificial intelligence algorithms to foresee possible medical crises like hypoglycemia episodes or heart attacks, usually before you even notice any symptoms. By facilitating actions that can halt the course of diseases, decrease hospitalisation rates, and total healthcare expenses, predictive healthcare is reshaping medicine from a reactive to a proactive paradigm. Aldali (2024) adds that AI systems may supplement predictive analytics with nuanced medical knowledge by extracting significant insights from unstructured clinical notes, research papers, and patient conversations through the integration of natural language processing (NLP).

Artificial intelligence (AI) has enormous promise in predictive healthcare, but there are obstacles to overcome. Data privacy, algorithmic bias, opaque decision-making (the "black box" problem), and the requirement for massive, high-quality databases are all major obstacles. When it comes to sensitive health information, it is extremely important to ensure the ethical use of patient data and to keep confidence in AI systems. In addition, AI models that are trained on data that does not reflect the population at large may provide biassed predictions, which might have an outsized impact on specific demographics and result in healthcare inequalities. In response to these worries, explainable AI (XAI) is gaining popularity; its goal is to increase the interpretability and transparency of AI judgements for the benefit of patients and doctors alike. To ensure the secure use of AI in healthcare settings, regulatory frameworks and global standards are likewise in the works. The smooth incorporation of AI tools into preexisting healthcare workflows is another obstacle. Data scientists, healthcare providers, lawmakers, and tech developers must work together for AI solutions to be user-friendly, relevant to clinical practice, and in line with healthcare organisations' objectives for successful deployment (Abbas, 2023).

Undoubtedly, AI-powered predictive healthcare solutions have a bright future ahead of them. Artificial intelligence (AI) will play a pivotal role in developing personalised medicine as data gathering methods and computing capabilities advance. It is believed that predictive models will improve in their ability to identify specific health risks and to personalise preventative plans when additional data from genomes, proteomics, and metabolomics is included. By improving patient selection and monitoring, speeding up clinical trials, and predicting medication efficacy and adverse effects, AI might also radically alter the pharmaceutical industry. Research on the use of predictive AI models in the field of mental health is ongoing. These models have the potential to analyse speech patterns and behavioural data in order to identify the early warning signals of mental health issues including anxiety and depression. Healthcare may be more easily accessed, particularly in underprivileged or rural regions, through the use of telemedicine systems that have been augmented using AI. These platforms can offer remote diagnosis and monitoring. The potential for these advancements to democratise healthcare, lessen healthcare disparities, and create a more resilient healthcare system that can handle the needs of both young and old populations is great (Chan, 2023).

Wu, Yutong. (2024) These days, artificial intelligence (AI) technology has a good effect on healthcare decision-making, early disease identification, and service optimisation. Many different forms of data may be used with artificial intelligence. This article has reviewed the latest developments in artificial intelligence (AI) technology, applications in healthcare, pros and cons, and ethical considerations. It has also shown how AI may improve diagnosis, personalised treatment, and prediction. Depending on the industry's approach to regulating AI in healthcare, this study's findings indicate that AI-ethics in healthcare will remain a hot subject for at least the next decade. The future of acceptable and approved AI health policies should be shaped by the moral and ethical discussions around AI healthcare laws.

Hasan, (2023) Modern medicine is being improved and revolutionised by artificial intelligence due to its capacity to comprehend, learn, act, and predict. This is the same regardless of whether the technology is utilised to guide robotic surgeons or to unearth previously unknown relationships between genes. Unlike humans, it is able to pick up on subtle patterns. This study investigates the many existing uses of AI in healthcare and the arguments around them. This study mainly focusses on two of the fastest growing fields of artificial intelligence in healthcare: AI-led pharmaceutical development and clinical trials and patient care. The findings show that AI has enhanced the medication development process by automating target identification for pharmaceutical businesses. In addition, AI has the potential to eliminate data monitoring operations that require a lot of manual labour. It has also been demonstrated that clinical trials utilising AI can handle massive data sets and produce very accurate results. Medical AI companies develop comprehensive solutions to help patients. Additionally, clinical intelligence examines medical information and provides patients with insights to enhance their quality of life.

Datta, (2019) Artificial intelligence (AI) is fast changing the healthcare industry due to its numerous potential applications. Diagnostic assistance, administrative simplification, personalised medicine, and predictive analytics are just a few of the various applications of artificial intelligence (AI) covered in this article. Concerns over patient data privacy and ethics, regulatory red tape, and the difficulties of integrating AI into existent systems are just a few of the several hurdles that must be surmounted before artificial intelligence (AI) can realise its immense potential in healthcare. Regardless of these challenges, AI has enormous potential for the future of healthcare, offering the chance to enhance treatment quality, decrease expenses, and boost patient outcomes. I see you're online.

Alloghani, (2020) As a result of the COVID-19 pandemic, healthcare organisations have been quick to use AI. Due to the growing popularity of remote care and monitoring and the pressing need for faster diagnosis and treatment, attention is shifting to artificial intelligence (AI) technologies that might enhance healthcare delivery and patient outcomes. Screening and diagnostics, drug research, and vaccine production have all been enhanced by AI-powered technologies such as computer vision, natural language processing, and predictive analytics, among others. Remote patient triage and treatment have also made use of chatbots and other virtual assistants driven by AI. While artificial intelligence (AI) has many positive impacts on healthcare, there are still certain problems that require fixing. This article will take a look at AI in healthcare, discussing its benefits and drawbacks, how widely accepted it is, and how careful its implementation must be due to ethical concerns. Although AI might completely transform healthcare, it is important to proceed with caution when using it. This research includes five case studies of well-known US hospitals that have used AI for different objectives.

Sharma, (2020) Artificial intelligence (AI) is rapidly becoming an essential part of digital health systems, which are influencing and bolstering modern medicine. Medical facilities are under increasing pressure from pandemics like COVID-19 to think about using technological solutions, such clinical decision support driven by artificial intelligence, to help doctors make better, faster judgements. The use of machine learning algorithms to medical data with the aim of bettering patient care and health outcomes is known as medical artificial intelligence (AI). In healthcare, AI is most commonly used for image analysis and clinical decision assistance. Clinical decision support systems facilitate the rapid acquisition of patient-specific information and research, which aids physicians with treatment, medicine, physical and mental health decisions, and other requirements. Artificial intelligence (AI) is used in medical imaging to assess X-rays, CT scans, MRIs, and other images/findings that a human radiologist might overlook. In response to global health crises like the COVID-19 pandemic, healthcare institutions throughout the world have begun developing new AI-powered technologies. Several AI-based healthcare applications will be covered in this article.

This method, together with 35 additional symptoms, may be used to assess the risk of various CVDs. Figure  depicts the proposed neutrosophic clinical decision-making workflow. Here we will get over the details.

Figure 1: Neutrosophic Clinical Decision-Making System: A Proposed Framework

This paper presents a new healthcare system that utilises WBAN for wireless data transfer and mobility. It gathers information from the user's wearable sensors. The medical gear includes sensors that can measure things like perspiration, blood pressure, heart rate, respiration rate, and breathing. In order to create a patient ID, the healthcare system's mobile interface app collects data and personal information from patients using sensors. After that, a smart gateway receives the data over Bluetooth and sends it to a cloud server for processing. The next step is for medical experts to determine the severity of the heart condition using the MADM approach in combination with IvTNN and WASPS. With the help of the decision-making system, the doctor may send the patient an electronic health record following the diagnosis. For patients with significant or life-threatening diseases, additional assessments may involve sending an ambulance to the hospital.

The four primary components of the model are data collecting, a smart eHealth gateway, a cloud server, and a cardiovascular disease prediction system based on the trapezoidal neutrosophic multi-attribute decision-making approach.

·                    Data Acquisition

If the user has many wearable sensors, it may collect their health data from all of them. The medical sensor nodes assess a number of indicators, such as heart rate, blood pressure, sweat, respiration rate, and movement activities. Through Wi-Fi or Bluetooth, it transmits data collected from the user's sensors to computers or mobile apps.

·                    Smart e-health Gateway

The smart gateway acts as a fog device that supports several communication protocols between the internet and the sensor network's touching point. Receiving medical data from different sub-networks and offering a high-level service for managing enormous data centres, which execute data processing regularly and temporarily, it operates as a go-between for data collection at the network's control and the cloud server. The fog gadget allows for early hospitalisation by transmitting data to the cloud and thus reducing reaction time.

·                    Cloud Server

The use of cloud computing has revolutionised the way medical practitioners access patient information. It can aid in backing up, storing, and maintaining health records. Included in the database's collection of personally identifiable information are the user's medical records. Its goal is to save the patient's monitoring data for a long period, much like it will give doctors diagnostic information. Analytics manages electronic health enrolment, which helps with the number of diagnoses and projections. Also, data visualisation is a key component of many statistical methods for proving that they have conducted thorough analyses.

We get some interesting results by comparing the current PSO and RBF-TSVM technique with the suggested OCSO and RBF-TSVM method.

(i) Accuracy

The ratio of correct results to incorrect ones is called accuracy. The accuracy of a situational evaluation is determined. In this case, this is the signal:

 Figure 2: Graph showing results for precision

When compared to the accuracy of the PSO, CSO, and GA-based approaches, as well as the suggested OCSO and RBF-TSVM based approach, the results are shown in Figure 4.1. On the Y-axis, we can see the relationship between accuracy and dataset size. By utilising OCSO for attribute reduction, the suggested method attained impressive precision. The findings showed that OCSO and based RBF-TSVM classification techniques outperformed the state-of-the-art method across all dataset sizes.

(ii) Sensitivity

The degree to which practical realities are duly acknowledged is one definition of sensitivity. This proves that the assessment is good at making predictions.

Figure 3: Scatter plot of sensitivity results

The outcomes of a comparison between the current PSO and RBF-TSVM based classification technique and the proposed OCSO and RBF-TSVM based classification method are displayed in Figure 4.2. The sensitivity is displayed on the Y-axis, while the dataset size is indicated on the X-axis. Additionally, RBF-TSVM is used to accomplish effective categorisation. In every dataset size, the suggested OCSO and based RBF-TSVM classification method outperformed the state-of-the-art system.

(iii) Specificity

Accuracy of the detected facts is a measure of specificity. This proves that the assessment is good at spotting troublesome situations.

Figure 4: Results visualisation for precision

Figure 4.3 shows a comparison of the accuracy of the current PSO and RBF-SVM based classification technique with that of the proposed OCSO and RBF-TSVM based approach. The dataset's quantity is shown on the X-axis, while its specificity is shown on the Y-axis. In every dataset size, the suggested OCSO and based RBF-TSVM classification method outperformed the state-of-the-art system.

Table 1 displays the results of OCSO's comparison with PSO, Table 5 displays the results with CSO, and Table 3 displays the results with GA.

Table 1:“The Outcomes of the OCSO and PSO Comparison”

Table 2: Examining OCSO and CSO: A Comparison”

Table 3:“Results of Analysing OCSO and GA in Comparison”

See Tables 4.4,4.5, and 4.6 for comparisons with PSO, CSO, and GA; OCSO shows a percentage improvement.

Table 4: Improving by a certain percentage Comparison of OCSO and PSO in Table

Table 5: Improving by a certain percentage Comparison of OCSO with CSO in Table

Table 6:“Improving by a certain percentage Summary of OCSO over GA”

Lastly, predictive healthcare solutions that use AI promise to gain substantially for the medical business. Through the use of complex pattern recognition, big data analytics, and machine learning algorithms, artificial intelligence (AI) may pave the way for more accurate diagnoses, quicker treatments, and improved patient outcomes in the healthcare system. These innovations in healthcare technology allow for preventative measures, which in turn save healthcare costs and enhance patient outcomes by identifying and mitigating potential health risks. With the use of AI-powered technologies, such as image recognition, natural language processing, and predictive analytics platforms, it is now feasible to forecast the start of chronic illnesses, the frequency of hospital readmissions, and the rate of disease progression. Before artificial intelligence (AI) is widely employed in healthcare, several things need to be thought out, including the importance of transparent and understandable models, the security of personal data, and ethical considerations. As AI develops further, it will be necessary for lawmakers, data scientists, and doctors to work together to ensure its safe and effective incorporation into healthcare systems so that patients are better served.