Learning-based Decision Support System Research Articles

Enhanced Fuzzy Score-Based Decision Support System for Early Stroke Prediction

According to the Global Health Observatory (GHO), stroke ranks second worldwide in causing dementia, right after Alzheimer's disease. The mortality rate linked to dementia resulting from stroke is high because symptoms are often recognized late, and stroke can be misinterpreted as other brain disorders. Early detection and diagnosis of stroke is crucial. Therefore, increasing awareness of stroke symptoms and implementing preventive measures becomes imperative. Prompt intervention by healthcare professionals can improve outcomes and reduce long-term complications of stroke. The research introduces an innovative approach for early stroke prediction using a fuzzy scoring-based Decision Support System. This approach encompasses three main modules: Mind-map based Data Modeling, Fuzzy scoring computing, and ML-based Decision System. By incorporating fuzzy logic, the approach extracts valuable knowledge from imprecise and uncertain data. Combining the fuzzy stroke risk model with a machine learning-based decision support system aims to enhance stroke prediction accuracy and improve preventive measures and patient outcomes. The approach's effectiveness was validated using real clinical data and tested with various machine learning classifiers, including K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The results showed a strong correlation between stroke cases and computed risk-scoring values. In comparison to predictions without fuzzy scoring and other related works, the stroke risk prediction using the proposed approach demonstrated higher accuracy, making it a promising method for early stroke detection and prevention.

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients

Background: Over the past three years, zinc deficiency among adolescents has varied based on region and access to healthcare. Globally, zinc deficiency affects approximately 2 billion people, leading to serious issues such as immune problems and growth delays, particularly in developing countries. In the U.S., around 10% of adolescents experienced zinc deficiency in 2021, with a higher prevalence among teenage girls. In Europe, deficiency rates are generally low but can be significant in Eastern Europe and Central Asia. In Asia, particularly in rural and low-income areas, deficiency rates range from 20 - 30%. In Turkey, the prevalence is high due to poor nutrition. Objectives: This study aimed to develop a machine learning-based decision support system to determine zinc deficiency in children and adolescents aged 10 - 18 years. Methods: This machine learning-based study was conducted with 370 adolescents aged 10 - 18 years to assess their zinc deficiency. The dataset consists of 8 feature vectors and an output vector. The machine learning methods used in the analysis include Logistic Regression, Naive Bayes, Decision Tree (CART), K-NN (K-Nearest Neighbors), SVM (Support Vector Machine), Gradient Boosting Classifier, AdaBoost Classifier, Bagging Classifier, Random Forest Classifier, MLP Classifier (Multilayer Perceptron), and XGB Classifier (XGBoost Classifier). Evaluation metrics such as accuracy, precision, recall, and F1 score were used to assess the performance of these methods. Including specific values for these metrics, such as "SVM achieved 94.6% accuracy," would allow readers to quickly compare the effectiveness of the models. Different metrics serve various purposes: Accuracy provides an overall view of performance, precision and recall highlight specific aspects, and the F1 score balances precision and recall. Results: The mean age of the patients in the dataset was 13.79 ± 1.18 years. Of the children, 64.32% (n = 238) were female and 35.68% (n = 132) were male. It was found that 62.7% (n = 232) of the children had low zinc levels, while 37.3% (n = 138) did not require zinc supplementation. Thirteen different machine learning methods were applied to a 70% training and 30% testing set. As a result, the SVM method provided the most successful outcome with 94.6% accuracy. Implementing the SVM-based system in pediatric clinics could improve efficiency and patient care by automatically detecting high-risk zinc deficiency patients based on lab results, providing early intervention alerts for faster treatment, and improving health outcomes. Highlighting these practical applications could increase the study’s appeal to healthcare professionals by demonstrating its real-world benefits. Providing detailed information on these applications would enhance the study’s clarity and practical value, making it more valuable for researchers and healthcare providers interested in AI tools for adolescent health. Conclusions: This study concluded that machine learning methods can effectively determine zinc deficiency in children. The SVM method demonstrated superior classification performance compared to the other methods. An SVM-based decision support system could be integrated into pediatric outpatient clinics to enhance diagnostic accuracy and patient care.

Optimization model for mass casualty management system using qos-aware routing protocol and casualty triage prediction

Abstract Purpose Unexpected events, whether man-made or natural, cause significant distress and disorder. The number and magnitude of disasters and catastrophes have been rising globally in recent decades, according to historical data. Continued real-time monitoring of mass casualty along with the arrangement of needed medical resources to handle the mass casualty is required to reduce the mortality and morbidity rates. Methods Electronic tag-based casualty monitoring and machine learning-based Decision Support Systems are emerging as a more effective and proactive solution that provides continuous remote monitoring of patients. A novel framework based on Body-to-Body Network, Prediction model, and Genetic Algorithm-based medical resource optimization is proposed for the continuous monitoring of the mass casualty and medical resource allocation at the incident. The aim of this work is to give priority to the handling of critical casualties. Firstly, a Quality of Service and load-sensitive routing protocol for transmitting mass casualties' physiological parameters across a wireless network is proposed, with the critical casualty being emphasized. Secondly, the clinical seriousness degree of the mass casualty is predicted using Backpropagation Artificial Neural Network. Finally, an optimization model using a Genetic algorithm and queuing theory is proposed to find the required optimal number of medical resources to handle critical and non-critical casualties separately. Also, the proposed optimization model considers the predicted clinical level transition rates of the mass casualty. Results The performance and accuracy of the proposed framework are evaluated using the MIMIC-II dataset. The outcome demonstrates that the framework emphasizes critical casualty management. Furthermore, the framework allocates an adequate number of servers by incorporating the proposed routing protocol in comparison to the AODV protocol. Conclusion The inclusion of a prediction model in the framework aids in allocating an adequate number of servers by considering the predicted clinical deteriorating transition rates of casualties at a mass casualty incident. In terms of the estimated length of the casualty at the incident, the results suggest that incorporating a medical resource optimization model outperforms the non-optimal option.

Deep Learning-Based Decision Support System for Automatic Detection and Grading of Surface Corrosion on Galvanized Steel Sheets

Corrosion in the sheets produced leads to significant material losses, including the loss of resources, capital, labor, energy and knowledge. Corrosion control is significant for sheets produced and sent to customers in iron and steel factories. Surface corrosion testing of produced sheets and the accurate detection of corrosion levels are of great importance. The corrosion detection process for sheets in steel factories is performed visually with the naked eye. This is a subjective and time-consuming method. Identifying corrosion damage by visual detection and accurately determining the type and extent of corrosion requires expertise. Wrong decisions at this stage lead to losses during the production phase. Therefore, there is a need for systems that can automate this process and make it human-independent. In this study, a decision support system was designed to automatically detect the level of corrosion in galvanized sheets using convolutional neural networks. The average accuracy of the system is 97.5%, the average precision is 0.98, the average recall is 1 and the average F1 score is 0.99. The results we obtained indicate that a successful system has been developed for the detection and determination of corrosion levels. The high performance of the convolutional neural network models used for corrosion detection supports the practical applicability of the developed system. This system will increase the reliability and efficiency of industrial processes by enabling the accurate and automatic classification of corrosion. This system, which meets a significant need in this area for industrial organizations, reduces production costs and also makes the corrosion detection process more consistent and faster.

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

ML Mental Health Support System: Stress Features Identification with COVID-19 Dataset and Selection Algorithms

The COVID-19 pandemic has caused significant changes in people's lives, resulting in everyone suffering from mental health issues such as stress, financial pressure, depression, frustration, and anxiety. Identifying critical features associated with mental stress can help healthcare professionals to develop effective intervention strategies. This paper aims to design a machine learning-based decision support system (DSS) to assess the mental health status of an individual after COVID-19.The primary objective of this work is to give an in-depth statistical analysis and performance evaluation of machine learning for stress prediction, with the ultimate goal of mitigating the adverse effects of stress on mental health. A survey was carried out on around 1,200 individuals. The research finding shows that age and work area significantly impact mental health. The result analysis was presented for different machine learning approaches in which the Naive Bayes classifier and Logistic Regression achieved the highest accuracy of 99% whereas the Artificial Neural Network (ANN) and Support Vector Machine (SVM) achieved 71% accuracy. Random Forest shows a good performance of 98% and k-Nearest Neighbors (k-NN) shows 75% accuracy. The evaluation results indicate that logistic regression, naive Bayes, and random forest demonstrate superior performance. This research could lead to the development of stress prediction and prevention solutions based on a Decision Support System (DSS).

Design and Implementation of the Employment Management Decision Support System based on Machine Learning

To address the challenges of current college student employment management, this study designed and implemented a machine learning-based decision support system for college student employment management. The system collects and analyzes multidimensional data, uses machine learning algorithms for prediction and matching, provides personalized employment guidance for students, and provides decision support for universities and enterprises. The research results indicate that the system can effectively improve the efficiency and accuracy of employment guidance, promote school-enterprise cooperation, and achieve a win-win situation for all parties.

Machine Learning-based Decision Support System for Healthcare in the Context of COVID-19: Case Study of Saudi Arabia

Machine Learning-based Decision Support System for Healthcare in the Context of COVID-19: Case Study of Saudi Arabia

AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree.

Asynclitism, a misalignment of the fetal head with respect to the plane of passage through the birth canal, represents a significant obstetric challenge. High degrees of asynclitism are associated with labor dystocia, difficult operative delivery, and cesarean delivery. Despite its clinical relevance, the diagnosis of asynclitism and its influence on the outcome of labor remain matters of debate. This study analyzes the role of the degree of asynclitism (AD) in assessing labor progress and predicting labor outcome, focusing on its ability to predict intrapartum cesarean delivery (ICD) versus non-cesarean delivery. The study also aims to assess the performance of the AIDA (Artificial Intelligence Dystocia Algorithm) algorithm in integrating AD with other ultrasound parameters for predicting labor outcome. This retrospective study involved 135 full-term nulliparous patients with singleton fetuses in cephalic presentation undergoing neuraxial analgesia. Data were collected at three Italian hospitals between January 2014 and December 2020. In addition to routine digital vaginal examination, all patients underwent intrapartum ultrasound (IU) during protracted second stage of labor (greater than three hours). Four geometric parameters were measured using standard 3.5 MHz transabdominal ultrasound probes: head-to-symphysis distance (HSD), degree of asynclitism (AD), angle of progression (AoP), and midline angle (MLA). The AIDA algorithm, a machine learning-based decision support system, was used to classify patients into five classes (from 0 to 4) based on the values of the four geometric parameters and to predict labor outcome (ICD or non-ICD). Six machine learning algorithms were used: MLP (multi-layer perceptron), RF (random forest), SVM (support vector machine), XGBoost, LR (logistic regression), and DT (decision tree). Pearson's correlation was used to investigate the relationship between AD and the other parameters. A degree of asynclitism greater than 70 mm was found to be significantly associated with an increased rate of cesarean deliveries. Pearson's correlation analysis showed a weak to very weak correlation between AD and AoP (PC = 0.36, p < 0.001), AD and HSD (PC = 0.18, p < 0.05), and AD and MLA (PC = 0.14). The AIDA algorithm demonstrated high accuracy in predicting labor outcome, particularly for AIDA classes 0 and 4, with 100% agreement with physician-practiced labor outcome in two cases (RF and SVM algorithms) and slightly lower agreement with MLP. For AIDA class 3, the RF algorithm performed best, with an accuracy of 92%. AD, in combination with HSD, MLA, and AoP, plays a significant role in predicting labor dystocia and labor outcome. The AIDA algorithm, based on these four geometric parameters, has proven to be a promising decision support tool for predicting labor outcome and may help reduce the need for unnecessary cesarean deliveries, while improving maternal-fetal outcomes. Future studies with larger cohorts are needed to further validate these findings and refine the cut-off thresholds for AD and other parameters in the AIDA algorithm.

Predictive Analytics for Demand Forecasting: A deep Learning-based Decision Support System

Demand forecasting is a critical component of supply chain management and business operations, enabling organizations to make informed decisions about production, inventory management, and resource allocation. In recent years, predictive analytics has emerged as a powerful tool for enhancing the accuracy and efficiency of demand forecasting. This review paper explores the transformative role of predictive analytics and deep learning in demand forecasting. It examines how these advanced techniques have evolved from traditional models based on past sales data, offering nuanced predictions through sophisticated statistical and machine learning methods. Deep learning, with its neural network structures, brings automatic feature learning, complex pattern handling, and scalability, enhancing forecasting in sectors like retail, manufacturing, and healthcare. The paper reviews various deep learning models, compares them with traditional methods, and discusses their impact on business operations and decision-making. It concludes by looking at future trends in predictive analytics and deep learning in demand forecasting.

Research on Advance Machine Learning Based Decision Support System for Frauds Detection and Prevention in Online Banking System

Research on Advance Machine Learning Based Decision Support System for Frauds Detection and Prevention in Online Banking System

Machine Learning Based Decision Support System for Coronary Artery Disease Diagnosis

Machine Learning Based Decision Support System for Coronary Artery Disease Diagnosis

Enhanced deep learning based decision support system for kidney tumour detection

This study presents a high-accuracy deep learning-based decision support system for kidney cancer detection. The research utilizes a relatively large dataset of 10,000 CT images, including both healthy and tumour-detected kidney scans. After data preprocessing and optimization, various deep learning models were evaluated, with DenseNet-201 emerging as the top performer, achieving an accuracy of 99.75 %. The study compares multiple deep learning architectures, including AlexNet, EfficientNet, Darknet-53, Xception, and DenseNet-201, across different learning rates. Performance metrics such as accuracy, precision, sensitivity, F1-score, and specificity are analysed using confusion matrices. The proposed system outperforms different deep learning networks, demonstrating superior accuracy in kidney cancer detection. The improvement is attributed to effective data engineering and hyperparameter optimization of the deep learning networks. This research contributes to the field of medical image analysis by providing a robust decision support tool for early and rapid diagnosis of kidney cancer. The high accuracy and efficiency of the proposed system make it a promising aid for healthcare professionals in clinical settings.

Deep Learning Empowered Decision Support Systems for Thyroid Cancer Detection and Management

Exploiting the capability of deep learning-based techniques, this research addresses an important and relevant problem on how to cost effectively detect thyroid cancer. In recent decades, there has been a significant increase in the incidence of thyroid cancer, prompting the need to understand its epidemiology. Existing studies have primarily employed qualitative techniques to investigate a single risk factor correlated with the disease development at a time. However, such an approach is inefficient and tends to ignore the interwoven connections among factors, resulting in a considerable disagreement with the identified risk factors among scholars. Additionally, the use of deep learning techniques in conjunction with medical imaging for computer-aided diagnostic (CAD) systems design has shown promises in detecting the disease, while there are research gaps regarding the detection of subtype and their co-existence. More importantly, existing CAD systems have shortcomings in adapting to different sample groups. To address these challenges, this research aims to shed light on the pathogenesis of thyroid cancer, enhance diagnostic performance, and improve generalisation of deep learning-based decision support systems. By harnessing the power of machine learning, specifically data mining and deep learning techniques, we seek to improve our understanding of the underlying mechanisms of the disease and develop innovative, robust, accurate, and efficient diagnostic tools. Extensive experiments indicate superior performance of the proposed methods than existing works. The systems proposed in this study have great impact to the wider society and contribute to the advancement of thyroid cancer research while enhancing clinical practice in related detection and subsequent management.

Enhancing the medical diagnosis of COVID-19 with learning based decision support systems

Since late December 2019, the COVID-19 pandemic has had substantial impact and long-lasting impact on numerous lives. The surge in patients has overwhelmed hospitals and exhausted essential resources such as masks and gloves. However, in response to this crisis, we have developed a robust solution that can ease the burden on emergency services and manage the influx of patients. Our proposed framework comprises deep learning and machine learning models that can predict and manage patient demand with high accuracy. The first model, is specifically designed to classify computed tomography (CT) scan images for COVID or non-COVID cases. We trained multiple convolutional neural network (CNN) models on a large dataset of CT scan images and evaluated their performance on a separate test set. Our evaluation showed that the ResNet50 model was the most effective, achieving an accuracy of 93.28%. The second model uses patient measurements dataset to predict the likelihood of intensive care unit (ICU) admission for COVID-19 patients. We experimented with the XGBoost machine learning algorithm and found that the accuracy score achieved 88.40%.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach

Introduction: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts’ subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. Method: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. Conclusions and Practical Applications: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Machine learning-based decision support system for orthognathic diagnosis and treatment planning

BackgroundDento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations.MethodsPatients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes.ResultsThe binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups.ConclusionsMachine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.

Solving diabetes diagnosis problems using machine learning

This research is devoted to the study of the use of machine learning methods to solve the problem of diagnosing diabetes. The results of using machine learning in the context of diabetes are varied and depend on the methods of data analysis, the models used and the quality of the data provided. Experiments on the Diabetes dataset were conducted in the study using a Naive Bayes classifier model and a linear kernel SVM for a binary classification problem. Models are trained on the training dataset, standardizing features, and evaluated on the test set using confusion, precision, recall, F1-measure, and AUC-ROC metrics. The results obtained confirm that machine learning can improve the accuracy of diagnosing diabetes and classifying its type. This allows for customized treatment plans to be developed, considering the unique characteristics of each patient. Machine learning models are also successful in predicting the likelihood of complications, allowing for preventative measures to be taken. Their use facilitates the integration of data from various sources, enriching patient information. In conclusion, machine learning-based decision support systems assist physicians and patients in making informed decisions.

Machine learning and text mining based real-time semi-autonomous staff assignment system

The growing demand for information systems has significantly increased the workload of consulting and software development firms, requiring them to manage multiple projects simultaneously. Usually, these firms rely on a shared pool of staff to carry out multiple projects that require different skills and expertise. However, since the number of employees is limited, the assignment of staff to projects should be carefully decided to increase the efficiency in job-sharing. Therefore, assigning tasks to the most appropriate personnel is one of the challenges of multiproject management. Assigning a staff to the project by team leaders or researchers is a very demanding process. For this reason, researchers are working on automatic assignment, but most of these studies are done using historical data. It is of great importance for companies that personnel assignment systems work with real-time data. However, a model designed with historical data has the risk of getting unsuccessful results in real-time data. In this study, unlike the literature, a machine learning-based decision support system that works with real-time data is proposed. The proposed system analyses the description of newly requested tasks using textmining and machine-learning approaches and then, predicts the optimal available staff that meets the needs of the project task. Moreover, personnel qualifications are iteratively updated after each completed task, ensuring up-to-date information on staff capabilities. In addition, because our system was developed as a microservice architecture, it can be easily integrated into companies? existing enterprise resource planning (ERP) or portal systems. In a real-world implementation at Detaysoft, the system demonstrated high assignment accuracy, achieving up to 80% accuracy in matching tasks with appropriate personnel.