Predictive Analytics In Healthcare Research Articles

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using ’filtered’ estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model’s efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

Patient Sickness Prediction Model

Abstract: The realm of healthcare constantly seeks innovative methods to improve patient care and outcomes. The advent of predictive analytics in healthcare has opened new avenues for preemptive diagnosis and treatment strategies. This paper introduces a novel Patient Sickness Prediction System (PSPS), designed to leverage machine learning algorithms and big data analytics for early detection of potential health issues. By integrating electronic health records (EHRs), real-time health monitoring data, and patient history, the PSPS aims to provide healthcare professionals with actionable insights, facilitating timely and personalised care. This research explores the system's development, implementation strategies, and its potential impact on healthcare delivery and patient outcomes.

DETECTA – MULTIPLE DISEASE DETECTION

Machine Learning Techniques for Predictive Analytics in Healthcare There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavor, but it can eventually assist practitioners in making timely decisions regarding patients' health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occurs due to a lack of medical infrastructure and a low ratio of doctors to the population. The statistics clearly show the same, WHO recommended, the ratio of doctors to patients is 1:1000 whereas India's doctor-to- population ratio is 1:1456, this indicates the shortage of doctors. The diseases related to heart, cancer, and diabetes can cause a potential threat to mankind, if not found early. Therefore, early recognition and diagnosis of these diseases can save a lot of lives. This work is all about predicting diseases that are harmful using machine learning classification algorithms. In this work, breast cancer, heart, and diabetes are included. To make this work seamless and usable by the mass public, our team made a medical test web application that makes predictions about various diseases using the concept of machine learning. In this work, our aim to develop a disease-predicting web app that uses the concept of machine learning-based predictions about various diseases like Breast cancer, Diabetes, Heart diseases, Covid, Pneumonia, Alzheimer.

Predictive analytics support for complex chronic medical conditions: An experience-based co-design study of physician managers’ needs and preferences

PurposeThe literature suggests predictive technology applications in health care would benefit from physician and manager input during design and development. The aim was to explore the needs and preferences of physician managers regarding the role of predictive analytics in decision support for patients with the highly complex yet common combination of multiple chronic conditions of cardiovascular (Heart) and kidney (Nephrology) diseases and diabetes (HND). MethodsThis qualitative study employed an experience-based co-design model comprised of three data gathering phases: 1. Patient mapping through non-participant observations informed by process mining of electronic health records data, 2. Semi-structured experience-based interviews, and 3. A co-design workshop. Data collection was conducted with physician managers working at or collaborating with the HND center, Danderyd University Hospital (DSAB), in Stockholm, Sweden. HND center is an integrated practice unit offering comprehensive person-centered multidisciplinary care to stabilize disease progression, reduce visits, and develop treatment strategies that enables a transition to primary care. ResultsInterview and workshop data described a complex challenge due to the interaction of underlying pathophysiologies and the subsequent need for multiple care givers that hindered care continuity. The HND center partly met this challenge by coordinating care through multiple interprofessional and interdisciplinary shared decision-making interfaces. The large patient datasets were difficult to operationalize in daily practice due to data entry and retrieval issues. Predictive analytics was seen as a potentially effective approach to support decision-making, calculate risks, and improve resource utilization, especially in the context of complex chronic care, and the HND center a good place for pilot testing and development. Simplicity of visual interfaces, a better understanding of the algorithms by the health care professionals, and the need to address professional concerns, were identified as key factors to increase adoption and facilitate implementation. ConclusionsThe HND center serves as a comprehensive integrated practice unit that integrates different medical disciplinary perspectives in a person-centered care process to address the needs of patients with multiple complex comorbidities. Therefore, piloting predictive technologies at the same time with a high potential for improving care represents an extreme, demanding, and complex case. The study findings show that health care professionals’ involvement in the design of predictive technologies right from the outset can facilitate the implementation and adoption of such technologies, as well as enhance their predictive effectiveness and performance. Simplicity in the design of predictive technologies and better understanding of the concept and interpretation of the algorithms may result in implementation of predictive technologies in health care. Institutional efforts are needed to enhance collaboration among the health care professionals and IT professionals for effective development, implementation, and adoption of predictive analytics in health care.

Diabetes Prediction Using Machine Learning Algorithm

bstract: Diabetes, a chronic metabolic disorder affecting millions worldwide, requires early detection and management to mitigate its complications. Machine learning (ML) techniques have emerged as promising tools for predictive analytics in healthcare, offering the potential to improve diagnostic accuracy and patient outcomes. This paper presents a comprehensive review of ML algorithms applied to diabetes prediction, encompassing diverse methodologies and datasets. The study evaluates the performance of various ML algorithms, including but not limited to logistic regression, decision trees, support vector machines, random forests, and deep learning approaches, in predicting the onset or progression of diabetes. Additionally, feature selection techniques and data pre-processing methods are explored to enhance model robustness and interpretability. Furthermore, this review highlights the significance of dataset characteristics such as size, imbalance, and feature diversity in influencing model performance. Challenges associated with model interpretability, scalability, and deployment in clinical settings are also discussed, alongside potential strategies to address these issues. The findings suggest that ML algorithms demonstrate promising capabilities in diabetes prediction, with many studies reporting high accuracy, sensitivity, and specificity. However, there remains a need for standardized evaluation metrics and benchmark datasets to facilitate comparisons across studies. Moreover, efforts to enhance model interpretability and address data privacy concerns are crucial for promoting the adoption of ML-based predictive models in healthcare practice. In conclusion, this review underscores the potential of ML techniques in diabetes prediction and emphasizes the importance of interdisciplinary collaboration between data scientists, clinicians, and healthcare stakeholders to leverage these advancements for improved patient care and disease management

A review of the use of machine learning in predictive analytics for patient health outcomes in pharmacy practice

Predictive analytics, empowered by machine learning, has emerged as a transformative force in healthcare, offering unparalleled opportunities for enhancing patient outcomes. The primary focus is on understanding the implications, applications, and challenges associated with the use of machine learning algorithms in predicting patient health outcomes. The paper begins by establishing the context with an overview of predictive analytics in healthcare and its evolution. Emphasis is placed on the critical role of patient health outcomes in pharmacy practice. The review explores the current landscape of predictive analytics in pharmacy practice, detailing traditional approaches, their limitations, and the advantages that machine learning brings to the forefront. An in-depth examination of applications follows, focusing on areas such as medication adherence prediction, disease progression modeling, and personalized medication regimens. Real-world case studies and success stories illustrate the practical impact of machine learning on patient outcomes. Addressing the importance of data sources, the paper discusses the diverse types of data employed in predictive analytics, ranging from electronic health records to patient-generated data and wearables. Ethical and privacy concerns are thoroughly explored, emphasizing the need for responsible data usage. The implications for pharmacists and healthcare providers are discussed, highlighting the evolving role of pharmacists in predictive analytics and the potential benefits and challenges for healthcare providers. The conclusion summarizes key findings and issues a call to action, encouraging further research and adoption of machine learning in pharmacy practice to harness its potential for improving patient outcomes.

Heart Disease Prediction Using GridSearchCV and Random Forest

INTRODUCTION: This study explores machine learning algorithms (SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest) for heart disease prediction, utilizing comprehensive cardiovascular and clinical data. Our research enables early detection, aiding timely interventions and preventive measures. Hyperparameter tuning via GridSearchCV enhances model accuracy, reducing heart disease's burdens. Methodology includes preprocessing, feature engineering, model training, and cross-validation. Results favor Random Forest for heart disease prediction, promising clinical applications. This work advances predictive healthcare analytics, highlighting machine learning's pivotal role. Our findings have implications for healthcare and policy, advocating efficient predictive models for early heart disease management. Advanced analytics can save lives, cut costs, and elevate care quality.
 OBJECTIVES: Evaluate the models to enable early detection, timely interventions, and preventive measures.
 METHODS: Utilize GridSearchCV for hyperparameter tuning to enhance model accuracy. Employ preprocessing, feature engineering, model training, and cross-validation methodologies. Evaluate the performance of SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest algorithms.
 RESULTS: The study reveals Random Forest as the favored algorithm for heart disease prediction, showing promise for clinical applications. Advanced analytics and hyperparameter tuning contribute to improved model accuracy, reducing the burden of heart disease.
 CONCLUSION: The research underscores machine learning's pivotal role in predictive healthcare analytics, advocating efficient models for early heart disease management.

Ensemble Learning Approaches for Improved Predictive Analytics in Healthcare

Ensemble Learning Approaches for Improved Predictive Analytics in Healthcare

Federated Learning for Predictive Healthcare Analytics: From theory to real world applications

In the contemporary landscape, machine learning has a pervasive impact across virtually all industries. However, the success of these systems hinges on the accessibility of training data. In today's world, every device generates data, which can serve as the building blocks for future technologies. Conventional machine learning methods rely on centralized data for training, but the availability of sufficient and valid data is often hindered by privacy concerns. Data privacy is the main concern while developing a healthcare system. One of the technique which allow decentralized learning is Federated Learning. Researchers have been actively applying this approach in various domains and have received a positive response. This paper underscores the significance of employing Federated Learning in the healthcare sector, emphasizing the wealth of data present in hospitals and electronic health records that could be used to train medical systems.

Machine Learning Algorithms for Early Prediction of Multiple Sclerosis Progression: A Comparative Study

Multiple Sclerosis (MS) is a chronic autoimmune disease characterized by central nervous system (CNS) degeneration, leading to diverse neurological symptoms. Managing MS poses a challenge due to its unpredictable progression. This study focuses on early prediction of MS progression using machine learning (ML) algorithms, comparing the effectiveness of Random Forest, XGBoost, Decision Tree, and Logistic Regression. Clinical, genetic, and environmental factors were analyzed in a cohort of Mexican mestizo patients recently diagnosed with Clinically Isolated Syndrome (CIS). Data preprocessing addressed missing values, and feature selection tailored to the population’s characteristics was applied. The dataset was split into training and testing sets, maintaining stratification for CDMS and nonCDMS cases. Machine learning models were trained with optimized hyperparameters. Performance evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, were employed. The Random Forest model exhibited superior performance (AUC: 0.93, accuracy: 87%), outperforming other models. Variable importance analysis identified top predictors, including Periventricular MRI, Age, Infratentorial MRI, and Oligoclonal Bands. The study’s clinical implications highlight ML’s potential in enhancing early MS prognosis, aiding timely interventions. The Random Forest model, with its robust performance, emerges as a valuable tool for identifying patients at risk. While the study contributes to predictive analytics in neurological disorders, limitations include cohort specificity and retrospective data use. Prospective studies and further exploration of data sources are recommended for broader applicability. This research demonstrates the efficacy of ML in early MS progression prediction, providing clinicians with a promising tool for personalized patient care. The findings contribute to advancing predictive healthcare analytics and emphasize the significance of tailored interventions in neurological disorders.

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

Objective. Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. Approach. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed. Main Results. The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals ‘at risk’ and ‘not at risk,’ respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model. Significance. This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

Developer Perspectives on Potential Harms of Machine Learning Predictive Analytics in Health Care: Qualitative Analysis.

Machine learning predictive analytics (MLPA) is increasingly used in health care to reduce costs and improve efficacy; it also has the potential to harm patients and trust in health care. Academic and regulatory leaders have proposed a variety of principles and guidelines to address the challenges of evaluating the safety of machine learning-based software in the health care context, but accepted practices do not yet exist. However, there appears to be a shift toward process-based regulatory paradigms that rely heavily on self-regulation. At the same time, little research has examined the perspectives about the harms of MLPA developers themselves, whose role will be essential in overcoming the "principles-to-practice" gap. The objective of this study was to understand how MLPA developers of health care products perceived the potential harms of those products and their responses to recognized harms. We interviewed 40 individuals who were developing MLPA tools for health care at 15 US-based organizations, including data scientists, software engineers, and those with mid- and high-level management roles. These 15 organizations were selected to represent a range of organizational types and sizes from the 106 that we previously identified. We asked developers about their perspectives on the potential harms of their work, factors that influence these harms, and their role in mitigation. We used standard qualitative analysis of transcribed interviews to identify themes in the data. We found that MLPA developers recognized a range of potential harms of MLPA to individuals, social groups, and the health care system, such as issues of privacy, bias, and system disruption. They also identified drivers of these harms related to the characteristics of machine learning and specific to the health care and commercial contexts in which the products are developed. MLPA developers also described strategies to respond to these drivers and potentially mitigate the harms. Opportunities included balancing algorithm performance goals with potential harms, emphasizing iterative integration of health care expertise, and fostering shared company values. However, their recognition of their own responsibility to address potential harms varied widely. Even though MLPA developers recognized that their products can harm patients, public, and even health systems, robust procedures to assess the potential for harms and the need for mitigation do not exist. Our findings suggest that, to the extent that new oversight paradigms rely on self-regulation, they will face serious challenges if harms are driven by features that developers consider inescapable in health care and business environments. Furthermore, effective self-regulation will require MLPA developers to accept responsibility for safety and efficacy and know how to act accordingly. Our results suggest that, at the very least, substantial education will be necessary to fill the "principles-to-practice" gap.

Predictive Analytics in Healthcare: Empowering Consultation with Machine Learning

The Smart Healthcare and Online Consultation initiative intends to offer patients a quick and convenient online platform for seeking medical advice and services. Real-time video consultations, appointment scheduling, prescription administration, and health records management are just a few of the capabilities available on the platform. To deliver individualized and superior healthcare services, the initiative to use cutting-edge such as AI, ML, and data analytics. By giving patients an easy and affordable way to receive healthcare services remotely, the Smart Healthcare and Online Consultation initiative has the potential to completely transform the healthcare sector.

The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols

The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.Clinical relevance- This platform is currently hosting a deep learning model for the early prediction of sepsis that is operational in two emergency departments.

Real-time Monitoring and Predictive Analytics in Healthcare: Harnessing the Power of Data Streaming

Real-time Monitoring and Predictive Analytics in Healthcare: Harnessing the Power of Data Streaming

Multi Disease Prediction Using HDO Machine Learning Approach

Several machine learning approaches can do predictive analytics on vast volumes of information in various sectors. Predictive analytics in health care is a challenging task. Still, it may ultimately aid physicians in making timely judgments about the health and handling of patients based on vast amounts of information. Breast cancer, diabetes, and heart-related disorders cause numerous fatalities worldwide, yet most of these decreases are attributable to an absence of appropriate screenings. The lack of remedial substructure and a short doctor-to-population proportion contribute to the issue above. Following WHO recommendations, physicians' ratio to affected persons should be in some range; India's doctor-to-public proportion indicates a doctor scarcity. Heart, cancer, and diabetes-related disorders pose a significant danger to humanity if not detected initially. Thus, early detection and identification of these disorders may save many lives. Using classification methods based on machine learning, the focus of this effort is to anticipate dangerous illnesses. Diabetes, heart disease, and breast cancer are discussed in this study. To make this effort easy and accessible to the general community, a web application for therapeutic tests has been developed that use machine learning to create illness predictions. In this study, a web application is created for illness prediction that employs the notion of machine learning-based forecasts for illnesses such as breast cancer, diabetes, and cardiovascular sickness.

Neutrosophic Hybrid Machine Learning Algorithm for Diabetes Disease Prediction

Because of its far-reaching effects, diabetes remains a major health problem on a worldwide scale. It's a metabolic illness that causes hyperglycemia and a host of other health issues, including cardiovascular disease, renal failure, and neuropathy. Many scientists have spent time and energy over the years trying to develop a reliable diabetes prediction model. Researchers are forced to adopt big data analytics and machine learning (ML)-based methodologies since there are still major open research concerns in this area owing to a lack of acceptable data sets and prediction techniques. This study seeks solutions by way of an examination of healthcare predictive analytics. The major purpose of this research was to explore the potential applications of big data analytics and machine learning-based approaches in the field of diabetes. In this study, we used the neutrosophic AHP as a feature selection method. The neutrosophic AHP is used to compute the importance of features, then apply the machine learning methods to these features. This study applied logistic regression, support vector machine (SVM), and random forest (RF) to predict the disease of diabetes.

ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations

Recent developments in big data technologies are revolutionizing the field of healthcare predictive analytics (HPA), enabling researchers to explore challenging problems using complex prediction models. Nevertheless, healthcare practitioners are reluctant to adopt those models as they are less transparent and accountable due to their black-box structure. We believe that instance-level, or local, explanations enhance patient safety and foster trust by enabling patient-level interpretations and medical knowledge discovery. Therefore, we propose the RObust Local EXplanations (ROLEX) method to develop robust, instance-level explanations for HPA models in this study. ROLEX adapts state-of-the-art methods and ameliorates their shortcomings in explaining individual-level predictions made by black-box machine learning models. Our analysis with a large real-world dataset related to a prevalent medical condition called fragility fracture and two publicly available healthcare datasets reveals that ROLEX outperforms widely accepted benchmark methods in terms of local faithfulness of explanations. In addition, ROLEX is more robust since it does not rely on extensive hyperparameter tuning or heuristic algorithms. Explanations generated by ROLEX, along with the prototype user interface presented in this study, have the potential to promote personalized care and precision medicine by providing patient-level interpretations and novel insights. We discuss the theoretical implications of our study in healthcare, big data, and design science.

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques.

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.