Comparative Analysis of Machine Learning Algorithms in Enhancing Healthcare Outcomes

Data analytics has emerged as a transformative force in optimizing hospital resource allocation and decision-making processes. The integration of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things has significantly enhanced the efficiency and effectiveness of healthcare delivery systems. These technologies enable hospitals to leverage vast amounts of data for improved clinical support, resource allocation, and operational efficiency. The implementation of data analytics in healthcare has led to several key benefits, including improved patient outcomes, cost reduction, enhanced predictive analysis capabilities, and more efficient resource optimization. However, the adoption of data analytics in healthcare settings faces several challenges, such as issues related to data quality and standardization, privacy and security concerns, and resistance to change within organizational structures. Addressing these challenges requires a comprehensive approach involving technological advancements, policy reforms, and cultural shifts within healthcare institutions. Emerging trends in healthcare data analytics point towards increased integration of artificial intelligence and deep learning technologies, promising to further enhance predictive modeling capabilities, real-time analytics, and the incorporation of diverse data sources for more precise and efficient healthcare delivery. While data analytics offers immense potential for optimizing hospital resource allocation and decision-making, its successful implementation necessitates ongoing research, interdisciplinary collaboration, and the development of robust frameworks to address ethical and practical challenges.

In this study, our aim is to predict sex using machine learning (ML) algorithms with morphometric variables taken from the vidian canal. The study was performed on Computed Tomography (CT) images of 137 women and 137 men aged 18–65 years. Seventeen morphometric variables were determined and recorded from axial and coronal skull images. These numerical data were used for sex determination with ML algorithms. As a result of the ML algorithm analysis of the data as 80% training set and 20% test set, the highest Accuracy (Acc.) rate was 0.89 with the Extra Tree Classifier (ETC) algorithm, and the lowest was 0.78 with Quadratic Discriminant Analysis (QDA). Among the parameters, the distance between the optic canal and the vidian canal was found to have the highest effect on sex determination with the Shapley Additive Explanations analyser. As a result of 10-fold cross-validation analysis of ML algorithms, the highest Acc rate was 0.82 with Random Forest (RF) algorithm, and the lowest was 0.73 with QDA. The findings of the study reveal that morphometric variables from the vidian canal show high accuracy in sex determination.

Big data analytics in health is an emerging area due to the urgent need to derive actionable intelligence from the large volumes of healthcare data to efficiently manage the healthcare system and to improve health outcomes. In this paper we present a ‘big’ healthcare data analytics platform—termed as Healthcare Analytics for Intelligence and Learning (HAIL)—that is an end-to-end healthcare data analytics solution to derive data-driven actionable intelligence and situational awareness to inform and transform health decision-making, systems management and policy development. The innovative aspects of HAIL are: (a) the integration of data-driven and knowledge-driven analytics approaches, (b) a sand-box environment for healthcare analysts to develop and test health policy/process models by exploiting a range of data preparation, analytical and visualization methods, (c) the incorporation of specialized healthcare data standards, terminologies and concept maps to support data analytics, and (d) text analytics to analyze unstructured healthcare data. The architecture of HAIL comprises the following four main modules (fig 1): (A) Health Data Integration module that entails a semantics-based metadata manager to synthesize health data originating from a range of healthcare institutions to formulate a rich contextualized data resource. The data integration is achieved through ETL workflows designed by health analysts and researchers, (B) Health Analytics Module provides a range of healthcare analytics capabilities including, (i) Exploratory Analytics using data mining to perform data clustering, classification and association tasks, (ii) Predictive Analytics to predict future trends/outcomes derived from past observations of the healthcare processes, (iii) Text Analytics to analyze unstructured texts (such as clinical notes, discharge summaries, referral notes, clinical guidelines, etc.), (iv) Simulation-based Analytics to simulate what-if questions based on simulation models, (v) Workflow analytics to interact with modeled clinical workflows to understand the affects of various confounding factors, (vi) Semantic Analytics to infer contextualized relationships, anomalies and deviations through reasoning over a semantic health data model, and (vii) Informational Analytics to present summaries, aggregations, charts and reports, (C) Data Visualization Module offers a range of interactive data visualizations, such as geospatial visualizations, causal networks, 2D and 3D graphs, pattern clusters and interactive visualizations to explore high dimensional data, (D) Data Analytics Workbench is an interactive workspace to enable data health analysts to specify and set-up their analytics process in terms of data preparation, selection and set-up of analytical methods and the selection of visualization methods. Using the workbench analysts can design sophisticated data analytics workflows/models using a range of data integration, analytical and visualization methods. The HAIL platform is available via a web portal and the desktop application, and it is deployed on a cloud infrastructure. HAIL can be connected with existing health data sources to provide front-end data analytics. Fig 2 shows the technical architecture. The HAIL platform has been applied to analyze a real-life clinical healthcare situations using actual data from the provincial clinical data warehouse. We will present two case studies of the use of HAIL. In conclusion, the HAIL platform addresses a critical need for healthcare analytics to impact health decision-making, systems management and policy development.

Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study

The healthcare industry generates massive amounts of data, from patient records to clinical trial results, which can be utilized to improve patient outcomes, reduce costs, and enhance the efficiency of healthcare delivery. Big data analytics is a technology that can help healthcare organizations make sense of these vast amounts of data. The potential benefits of big data analytics in healthcare include improved patient outcomes, reduced costs, enhanced efficiency, and more accurate diagnoses. However, big data analytics also presents significant challenges, including data privacy and security, and the complexity of healthcare data. This research paper aims to explore the role of big data analytics in healthcare, focusing on its potential benefits, challenges, and applications. By understanding the potential of big data analytics in healthcare and the challenges it presents, healthcare organizations can leverage this technology to improve healthcare delivery and patient outcomes.

The rapid progress in artificial intelligence (AI) and data analytics has revolutionized healthcare and made predictive insights possible that enhance clinical decision-making by means of real-time patient monitoring. This paper describes the development of an artificial intelligence-driven healthcare tracker meant to collect, evaluate, and interpret patient data for proactive medical control. The proposed system effectively forecasts future health dangers, monitors key health indicators, and uses big data analytics and machine learning algorithms to enhance early disease identification. The main purposes of the tracker are feature engineering, automated data purification, and augmentation, therefore ensuring the dependability and robustness of healthcare data sets. Combining wearable sensors, electronic health records (EHRs), and cloud computing, the system offers customized recommendations and real-time health status updates. Furthermore, utilized to identify patterns in patient data are predictive modeling techniques, therefore supporting early intervention and preventive therapy projects. Comparatively to traditional monitoring systems, experimental results show that the AI-driven healthcare tracker greatly increases diagnosis accuracy, patient participation, and clinical efficiency. Using many machine learning metrics, the model's performance is evaluated and shows notable progress in anomaly detection, disease prediction, and customized healthcare recommendations. The proposed approach might change remote patient monitoring, lower hospital readmissions, and improve healthcare resource economy. This study emphasizes the transforming power of artificial intelligence and data analytics in healthcare, therefore enabling the creation of more smart, flexible, and data-driven healthcare solutions. Later studies will focus on enhancing the functionality of the system by means of deep learning models and real-time artificial intelligence decision support systems thus raising prediction accuracy and patient outcomes.

The objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, viz., multi-layer perceptron (MLP), random forest (RF), extra trees regressor (ETR), CatBoost, and gradient boost (GB), considering the impact of variation in sample sizes for theprediction of soil nutrients. In this context, this study evaluates the impact of sample size on the prediction performance of five ML algorithms for mapping 14 soil properties, including key soil physico-chemical properties (soil organic carbon, pH, and electrical conductivity), and multiple macro (available nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur) and micronutrients (available iron, manganese, copper, zinc, and boron) at a geographical extent of 8303 km2 located in eastern India. A total of 1024 surface soil samples were collected, of which 800 were used for model training, while the remaining 224 were reserved for independent validation (IV) of the resultant maps. The original training data set (800 samples) was reduced by random selection into six different sample sizes, i.e., 800, 400, 200, 100, 50, and 25. An exhaustive set of 574 environmental variables, derived from digital terrain derivatives and Sentinel-2 satellite imagery, was used as predictors. Popular statistical indicators, such as Lin's concordance correlation coefficient (CCC) and root mean squared error (RMSE) were employed to evaluate the predictive capability of the algorithms under different sample size scenarios. The results showed that prediction accuracy and reliability of prediction performance across multiple target variables improved with sample size. However, beyond a certain point, improvement in predictive performance became substantially negligible compared to the efforts in additional sampling. All the ML algorithms performed well (mean IV-CCC varied between 0.26 and 0.64 for different soil properties) to increase insample size, except MLP, which exhibited a poorer prediction performance(mean IV-CCC varied between 0.14 and 0.29 for different soil properties). Micronutrients in general responded well to the increase in sample sizes. The uncertainty analysis revealed that increasing sample size generally reduced prediction uncertainty, though the extent varied by soil property and ML algorithm. Concisely, the results presented herein showed an effective manner of selecting an appropriate sample size and a suitable ML algorithm to predict multiple soil nutrients accurately, which would be recommended to achieve optimal accuracy for a project.

The healthcare industry generates vast amounts of data that are crucial for improving patient outcomes and advancing medical research. However, traditional on premise solutions for data storage and analysis can become inadequate to handle the increasing volume, variety and velocity of healthcare data. The study aims to investigate the potential benefits and challenges of using cloud-based solutions for data analytics in healthcare. This paper reports about latest development and detailed role of using Artificial intelligence and capabilities of cloud Computing in health care sector/industry to foster innovative thinking, optimum wellbeing of the patient, focused medicinal support. This paper discusses various applications, algorithms and future of big data analytics with a focus on architecture, application and applicability of big data analytics using Hadoop and Cloud Computing in healthcare industry such as monitoring, prediction, performance, management etc including intensive care unit. many cloud platforms, like MMAP, are working in this field to provide a fast, reliable cost effective, efficient, and patient centric and solution to community health issues with capability of forecasting the health impact of various diseases on community for a given region or nation. Cloud computing framework, along with Artificial intelligence and Hadoop, aids healthcare management in completing analytical computations to identify logical, pertinent, and factual trends essential to strategize and enhanced readiness in event of catastrophes by facilitating data exchange among all stake holders.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

Diabetes is a long-term health condition that impacts millions of people worldwide, and correct and timely diagnosis is required to manage it effectively. In the present research study, we propose an improved model of diabetes prediction based on the usage of Big Data Analytics (BDA) and Machine Learning (ML) algorithms for enhancing the predictability of diabetes. Our research is based on the ideas developed in the reference paper, where the application of BDA in enabling healthcare decisionmaking was proposed. With the inclusion of the usage of advanced ML algorithms like Decision Trees, Random Forest, Logistic Regression, Naive Bayes, and Artificial Neural Networks (ANN), we enhance predictability for the diagnosis of diabetes. We employed a comprehensive healthcare dataset with various health metrics, including blood pressure, cholesterol, BMI, smoking, and physical activity. The data was heavily preprocessed, ranging from feature engineering to normalization, to get better model performance. All the models were trained and tested on accuracy, precision, recall, and ROC-AUC. Among the models, Random Forest and ANN were better with higher accuracy, detecting diabetic patients at risk effectively. Our model not only increases prediction accuracy but also facilitates scalability with Big Data technology and practices. The ANN model, in particular, proved effective in finding complex patterns within the data, yielding a reliable solution towards real-time diabetes prediction. This effort illustrates the potential of integrating BDA and ML in healthcare for data-driven decision-making and supporting early diagnosis and management of diabetes. The model is deployed as an interactive user-friendly web application using Streamlet, where users can input health indicators and receive predictions from different models. Through its interactive interface, it facilitates healthcare professionals and patients by offering themreal-time information, thereby ultimately improving diabetes management and patient care.

A huge volume of digitized clinical data is generated and accumulated rapidly since the widespread adoption of Electronic Medical Records (EMRs). These big data in healthcare hold the promise of propelling healthcare evolving from a proficiency-based art to a data-driven science, from a reactive mode to a proactive mode, from one-size-fits-all medicine to personalized medicine. This paper first discusses the research background - big data analytics in healthcare, the research framework of big data analytics in healthcare, analysis of medical process, and the literature summary of treatment pattern mining. Then the challenges for data-driven typical treatment pattern mining are highlighted, including similarity measure between treatment records, typical treatment pattern extraction, evaluation and recommendation, when considering the rich temporal and heterogeneous medical information in EMRs. Furthermore, three categories of typical treatment patterns are mined from doctor order content, duration, and sequence view respectively, which can provide a data-driven guideline to achieve the “5R” goal for rational drug use and clinical pathways.

Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system.

It is clear that machine learning (ML) has influenced every aspect of our lives! From reviewing the weather report to seeking a loan or a credit card, machine learning is utilized in nearly every facet of our everyday lives. This chapter examines ML through its algorithms and applications. Particular attention is focused on ML applications within the healthcare data analytics sector, encompassing new and emerging diseases. The chapter highlights the importance of random forest as a powerful machine learning classification technique for several diseases. Enhancing the efficiency and performance of random forest by utilizing only a portion of the model (via a technique known as pruning) has shown success in two approaches: CLUstering-based diverse random forest (CLUB-DRF) and evolutionary game theoretic approach to random forest pruning (eGAP). Owing to pruning, both CLUB-DRF and eGAP exhibit substantially reduced model sizes compared to the unpruned random forest, while consistently achieving better predictive performance. We experimentally illustrate the effectiveness of these two approaches on three diseases.

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

Cross-Site Scripting (XSS) attacks pose a significant cybersecurity threat by exploiting vulnerabilities in web applications to inject malicious scripts, enabling unauthorized access and execution of malicious code. Traditional XSS detection systems often struggle to identify increasingly complex XSS payloads. To address this issue, this research evaluated the efficacy of Machine Learning algorithms in detecting XSS threats within online web applications. The study conducts a comprehensive comparative analysis of XSS attack detection using four prominent Machine Learning algorithms, which consist of Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). This research utilizes a comparative methodology to assess the selected Machine Learning algorithms by analyzing their performance metrics, including confusion matrix, 10-fold cross-validation, and assessment of training time to thoroughly evaluate the models. By exploring dataset characteristics and evaluating the performance metrics of each selected algorithm, the study determined the most robust Machine Learning solution for XSS detection. Results indicate that Random Forest is the top performer, achieving 99.93% accuracy and balanced metrics across all criteria evaluated. These findings will significantly enhance web application security by providing reliable defenses against evolving XSS threats.