Decision Support System For Prediction Research Articles

AI-Assisted Survival Prediction in Colorectal Cancer: A Clinical Decision Support Tool

Purpose: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Accurate survival prediction is crucial for advanced-stage patients to optimize treatment strategies and improve clinical outcomes. This study aimed to develop an artificial intelligence-assisted clinical decision support system (CDSS) for survival prediction in CRC patients using clinical and genomic data from the Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD) dataset. Methods: Machine learning algorithms, including C4.5 Decision Tree, Support Vector Machines (SVM), Random Forest, and Naive Bayes, were employed to create survival prediction models. Clinical parameters and genomic data from key pathways, such as glycolysis/gluconeogenesis and mTORC1, were integrated into the models. The models were evaluated based on accuracy and performance. Results: The Random Forest algorithm achieved the highest accuracy (82.3%) when only clinical parameters were used. When clinical data were combined with gene expression data, the model’s accuracy increased further. The resulting models were incorporated into a user-friendly web interface, SurvCOCA, for clinical use. Conclusions: This study demonstrates the potential of AI-based tools to improve prognosis predictions in CRC patients. Further research is needed, with larger datasets and additional machine learning algorithms, to enhance clinical decision-making and optimize treatment strategies.

Design of Interoperable Electronic Health Record (EHR) Application for Early Detection of Lung Diseases Using a Decision Support System by Expanding Deep Learning Techniques.

Electronic health records (EHRs) are live, digital patient records that provide a thorough overview of a person's complete health data. Electronic health records (EHRs) provide better healthcare decisions and evidence-based patient treatment and track patients' clinical development. The EHR offers a new range of opportunities for analyzing and contrasting exam findings and other data, creating a proper information management mechanism to boost effectiveness, quick resolutions, and identifications. The aim of this studywas to implement an interoperable EHR system to improve the quality of care through the decision support system for the identification of lung cancer in its early stages. The main objective of the proposed system was to develop an Android application for maintaining an EHR system and decision support system using deep learning for the early detection of diseases. The second objective was to study the early stages of lung disease to predict/detect it using a decision support system. To extract the EHR data of patients, an android application was developed. The android application helped in accumulating the data of each patient. The accumulated data were used to create a decision support system for the early prediction of lung cancer. To train, test, and validate the prediction of lung cancer, a few samples from the ready dataset and a few data from patients were collected. The valid data collection from patients included an age range of 40 to 70, and both male and female patients. In the process of experimentation, a total of 316 images were considered. The testing was done by considering the data set into 80:20 partitions. For the evaluation purpose, a manual classification was done for 3 different diseases, such as large cell carcinoma, adenocarcinoma, and squamous cell carcinoma diseases in lung cancer detection. The first model was tested for interoperability constraints of EHR with data collection and updations. When it comes to the disease detection system, lung cancer was predicted for large cell carcinoma, adenocarcinoma, and squamous cell carcinoma type by considering 80:20 training and testing ratios. Among the considered 336 images, the prediction of large cell carcinoma was less compared to adenocarcinoma and squamous cell carcinoma. The analysis also showed that large cell carcinoma occurred majorly in males due to smoking and was found as breast cancer in females. As the challenges are increasing daily in healthcare industries, a secure, interoperable EHR could help patients and doctors access patient data efficiently and effectively using an Android application. Therefore, a decision support system using a deep learning model was attempted and successfully used for disease detection. Early disease detection for lung cancer was evaluated, and the model achieved an accuracy of 93%. In future work, the integration of EHR data can be performed to detect various diseases early.

An MRI-based multi-parameter clinical decision support system (NeoMDSS) for early prediction of pathological complete response after the first cycle of neoadjuvant therapy in breast cancer: A multi-center prospective observational cohort study.

592 Background: Currently, there is no reliable and convenient way to predict response rates in early stages of neoadjuvant therapy (NAT) for breast cancer, which can affect the prognosis of non-responsive patients. Methods: In this prospective multi-center observational study, eligible participants who underwent NAT followed by radical surgery were recruited. All patients underwent magnetic resonance imaging (MRI) examination before and after the first cycle of NAT (1st-NAT). We analyzed the effects of clinicopathological features, such as histological grade, hormone receptor (HR) and HER2 status, Ki-67 expression, blood cell analysis, and MRI parameters including the reduction ratio of tumor diameter (ΔD%), apparent diffusion coefficient (ADC), and early enhancement ratio (EER) on NAT. By incorporating independent influencing factors, we developed a multiparametric clinical decision support system (NeoMDSS) model based on a retrospective cohort and validated its accuracy in both a prospective single-center internal cohort and a prospective multi-center external cohort. Relevant clinical trial details can be found under the identifier NCT04909554. Results: A total of 301 breast cancer patients were enrolled between January 2019 and December 2023, including 140 patients in the training cohort, 120 patients in the internal validation cohort, and 41 patients in the external validation cohort. The NeoMDSS model demonstrated excellent performance in predicting pathological complete response (pCR) at 1st-NAT, with an AUC of 0.874 (95% CI 0.813-0.935) in the training cohort, 0.845 (95% CI 0.771-0.919) in the internal validation cohort, and 0.867 (95% CI 0.742-0.992) in the external validation cohort. The NeoMDSS model exhibited a sensitivity, specificity, and accuracy of 80.0%, 81.3%, and 80.5%, respectively, in the internal validation cohort, and 87.0%, 83.3%, and 85.4%, respectively, in the external validation cohort. Calibration curves and decision curve analysis (DCA) further supported the clinical value of the NeoMDSS model. To facilitate the clinical application of the NeoMDSS model, a nomogram and an online website ( www.gdphneomdss.com ) were developed to calculate the probability of pCR, which the sensitivity, specificity, and accuracy of the NeoMDSS model were all 80.0% in the internal validation cohort and 80.0%, 81.3%, and 80.5% in the external validation cohort. Conclusions: The NeoMDSS model serves as a convenient tool in clinical practice at 1st-NAT to calculate and accurately predict the probability of pCR after NAT. This assists clinicians in determining whether to modify the treatment plan and improving personalized treatment for patients with poor response to NAT. Clinical trial information: NCT04909554 .

Development of a Medical Condition Prediction Model Using Natural Language Processing with K-Nearest Neighbour

Capturing the effect of drugs being used by patients and using this review to predict the medical ailment they are facing is a good approach to easily predict medical conditions. A lot of researchers use clinical and demographic data (risk factors) to predict diseases, the limitation of this approach is that not all the instances would have the right clinical results and there is usually missing values, low prediction accuracy, inadequately pre-processed dataset, failure to consider feature selection and un-experimentation of alternative values of K when using K-nearest neighbour. Using drug review would go a long way as their effect and symptoms as reported by the user through their review would capture relevant information needed. This study employed an open access drug review dataset to predict the medical condition, this dataset consist of training and testing split which was integrated and later split using 80-20 splitting with stratification. The dataset went through some natural language processing techniques such as lemmatization, stemming, removal of stop words, tokenization, and vectorization among others. Forward –backward feature selection technique was employed with the comments having significant effect to the prediction of the condition. K-nearest neighbour was then employed to predict the medical condition using the drug review as the feature with the condition as the target variable. Different values of nearest neighbours were used to train the model with k=1 given the best predictive average accuracy of 89% with weighted average precision of 90%. The model gave the same average accuracy of 84% when k was initialised to 3, 4, 5 and 6 respectively. Moreover, the model obtained a better result when compared with exciting systems. Therefore, with the use of artificial intelligence, medical doctors and patients can easily use drug review to predict certain medical condition using clinical predictive decision support system.

DAWN: Dashboard for Agricultural Water Use and Nutrient Management—A Predictive Decision Support System to Improve Crop Production in a Changing Climate

Abstract Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.

Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study

The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell’s C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions.

An Innovative Ensemble Deep Learning Clinical Decision Support System for Diabetes Prediction

An Innovative Ensemble Deep Learning Clinical Decision Support System for Diabetes Prediction

Tilime crop yield prediction using machine learning algorithms

Agriculture stands as the bedrock of Malawi's economy, involving nearly 90% of the population in subsistence farming. However, the sector faces challenges arising from unpredictable weather patterns, climate shifts, and environmental factors that threaten its sustainability. This paper proposes a pioneering solution leveraging Machine Learning (ML) to address these challenges, presenting a robust decision support system for Crop Yield Prediction (CYP). By harnessing ML capabilities, the system aids in crucial decisions related to crop selection and management throughout the growing season, specifically tailored for the unique agricultural landscape of Malawi. This approach aims to empower farmers by providing valuable insights into soil quality, composition, and nutrients, enabling informed decisions to maximize crop yield. Through the integration of advanced technology into the agricultural domain, this paper seeks to usher in a transformative era for Malawian agriculture, fostering resilience and sustainability in the face of evolving environmental dynamics.

Urinary Bladder Inflammation Prediction with the Gray Wolf Optimization Algorithm and Multi-Layer Perceptron-Based Hybrid Architecture

In this study, a decision support system for bladder inflammation prediction is presented. The proposed decision support system is built by establishing a hybrid architecture with Gray wolf optimization algorithm (GWO) and Multi-layer perceptron (MLP) networks. In addition to optimizing the hyperparameters in the MLP structure with GWO, the hybrid architecture also optimizes the order of input values to be presented to the MLP structure. The Acute Inflammations data set in the UCI Machine Learning repository was used as the data set in the study. Classification operations were carried out on this data set with the models obtained with hybrid architecture, Decision trees, k-Nearest Neighbors and Support Vector Machines methods. The controversial findings presented as a result of experimental studies have shown that the proposed hybrid architecture produces more successful results than other machine learning methods used in the study. In addition, the MLP network structure optimized with the hybrid architecture offers a new diagnostic method in terms of patient decision support systems.

Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images

The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization algorithm (GSOA) technique for metaheuristic-based FS. Glaucoma disease is selected as the subject of investigation as this disease is spreading worldwide at a very fast pace; 111 million instances of glaucoma are expected by 2040, up from 64 million in 2015. It causes widespread vision impairment. Optic nerve fibres can be degraded and cannot be replaced later in this disease. As a starting point, the retinal fundus images of glaucoma infected persons and healthy persons are used, and 36 features were retrieved from these images of public benchmark datasets and private dataset. Six ML models are trained for classification on the basis of the GSOA's returned subset of features. The suggested FS technique enhances classification performance with selection of most influential features. The eight statistical performance evaluating parameters along with execution time are calculated. The training and testing have been performed using a split approach (70:30), 5-fold cross validation (CV), as well as 10-fold CV. The suggested approach achieved 95.36 % accuracy. Due to its auspicious performance, doctors might use the suggested method to receive a second opinion, which would also help overburdened skilled medical practitioners and save patients from vision loss.

A decision support system for osteoporosis risk prediction using machine learning and explainable artificial intelligence

Osteoporosis is a metabolic bone condition that occurs when bone mineral density and mass decrease. This makes the bones weak and brittle. The disorder is often undiagnosed and untreated due to its asymptomatic nature until the manifestation of a fracture. Machine Learning (ML) is extensively used in diverse healthcare domains to analyze precise outcomes, provide timely risk scores, and allocate resources. Hence, we have designed multiple heterogeneous machine-learning frameworks to predict the risk of Osteoporosis. An open-source dataset of 1493 patients containing bone density, blood, and physical tests is utilized. Thirteen distinct feature selection techniques were leveraged to extract the most salient parameters. The best-performing pipeline consisted of a Forward Feature Selection algorithm followed by a custom multi-level ensemble learning-based stack, which achieved an accuracy of 89 %. Deploying a layer of explainable artificial intelligence using tools such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance provided interpretability and rationale behind classifier prediction. With this study, we aim to provide the holistic risk prediction of Osteoporosis and concurrently present a system for automated screening to assist physicians in making diagnostic decisions.

Internet-of-things based machine learning enabled medical decision support system for prediction of health issues

Internet-of-things based machine learning enabled medical decision support system for prediction of health issues

A data-driven decision support system for service completion prediction in last mile logistics

A data-driven decision support system for service completion prediction in last mile logistics

An Intelligent Decision Support System for Crop Yield Prediction Using Machine Learning and Deep Learning Algorithms

Agriculture is crucial to economic growth and development. Crop yield forecasting is critical for food production which includes vegetables, fruits, flowers, and cattle. Artificial Intelligence (AI) is rising in agriculture, providing farmers with real-time or long-term insights about their fields. It allows us to identify the areas that require irrigation, fertilization, or pesticide treatment. Statistical models struggle to track complex relationships in crop yields due to numerous factors. Machine Learning (ML) and Deep Learning (DL) algorithms can solve this problem by training themselves in these relationships, enabling accurate predictions in agricultural yield prediction methods. Predicting product performance in agriculture is challenging due to various factors, but profit forecasting improves decision-making, production, economics, and food safety. The present study focuses on the use of ML and DL algorithms to suggest a novel decision support system for crop yield prediction with the objectives to develop a robust, accurate model, investigate algorithm effectiveness, and create a user-friendly system for informed crop production decisions. According to the results, the developed system is capable of making precise predictions, which can support farmers in making better decisions about how to manage their crops. The simulation results demonstrate that the intelligent decision support system proposed for crop yield prediction using ML and DL algorithms is capable of achieving high accuracy and precision. The system can be used to help farmers make better decisions about crop planting and management, which can lead to increased crop yields and profits. The results of our experiment show that our model is better than the others and it achieves an accuracy of 99.82 %. Additionally, we utilized ML to condense the input space while preserving high accuracy.

Deep Learning-Based Education Decision Support System for Student E-learning Performance Prediction

Information Technology (IT) and its advancements change the education environment. Conventional classroom education has been transformed into a modernized form. Education field decision-makers are always searching for new technologies that provide fast solutions to support Education Decision Support Systems (EDSS). There is a significant need for an effective decision support system to utilize student data which helps the university in making the right decisions. The Electronic learning system (e-learning) provides a live forum for faculties and students to connect with learning portals and virtually execute educational activities. Even though these modern approaches support the education system, active student participation still needs to be improved. Moreover, accurately measuring student performance using collected attributes remains difficult for parents and teachers. Therefore, this paper seeks to understand and predict student performance using effective data processing and a deep learning-based decision model. The implementation of EDSS starts with data preprocessing, Extraction-Transformation-Load (ETL), a data mart area to store the extracted data with Online Analytical Processing (OLAP) processing, and decision-making using Deep Graph Convolutional Neural Network (DGCNN). The statistical evaluation is based on the student dataset from the Kaggle repository. The analyzed results depict that the proposed EDSS model on an independent data mart with efficient decision support and OLAP provides a better platform to make academic decisions and help educators to make necessary decisions notified to the students.

A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction

Feature selection is one of the crucial data preprocessing techniques for improving the performance of machine learning (ML) models. Recently, metaheuristic feature selection algorithms have become popular because they select optimal features for ML problems. This paper presents three feature selection strategies based on metaheuristic algorithms: Bacterial Foraging (BFOA), Emperor Penguin (EPO), and a hybrid (hBFEPO) combining BFOA and EPO. The baseline algorithms have been investigated for feature selection in other ML tasks, but not for breast cancer classification. A hybrid of these two has been used for the first time. These strategies were initially tested on the COVID-19 dataset. After achieving satisfactory results, these strategies are evaluated on the WDBC Breast Cancer dataset. The performance of our models on WDBC is compared with recent eighteen state-of-the-art studies. The results indicate that the hBFEPO model outperforms other models, achieving 100% precision and specificity, 98.49% accuracy, 95.43% sensitivity, a 95.99% F1-score, and a 99.60% AUC.

CLINICAL DECISION SUPPORT SYSTEM (CDSS) FOR HEART DISEASE DIAGNOSIS AND PREDICTION BY MACHINE LEARNING ALGORITHMS: A SYSTEMATIC LITERATURE REVIEW

Clinical decision support systems rooted in machine learning have grabbed much attention due to the use of huge datasets for performing analysis and accurate diagnosis of diseases. Our recent research contributed to the existence of research by having a complete assessment of decision support systems used within the clinical context to diagnose cardiovascular diseases. The researchers separately compiled and analyzed aspects related to clinical decision support systems (CDSS) for cardiac disorders. The primary intention of this systematic review is to point out and critically evaluate the Machine Learning (ML)-based PubMed, Clinical Trial.gov, and Cochrane libraries, an analysis of 400 different studies (1990 to 24 January 2021) is made in which 25 papers met the inclusion criterion. The technique incorporated here NVIVO 10 software gathers and analyzes data. Search filters such as “Machine Learning”, “Clinical decision support systems”, and “heart disease prediction” are used for heart diseases. The results were obtained within clinics in 55% of such investigations, whereas experimental setups were seen in 25% of the studies. The remaining 20% research did not report on the methodologies’ relevance and effectiveness in healthcare situations. The given findings point to CDSS ability to provide accurate interpretations and accurate visual peer representations. The key finding of this analysis report is that although CDSS might be employed in clinical settings it needs to be properly trained with non-ambiguous real-time clinical data. The creation of fuzzy complexes that can conclude on clinical deviations many of which are practical and imposed and tracked in real time is a major focus of future Machine Learning-based CDSS research.

OnionAider: A Model Driven Decision Support System for Weather and Pest-Occurrence Prediction in Onion Cultivation

The cultivation of onions is crucial for meeting global demand, as they are widely consumed and used as a seasoning in various dishes worldwide. However, the production of onions is significantly affected by climatic factors and pest infestations. To address these challenges, this study presents OnionAider, a Model Driven Decision Support System that utilizes weather and pest-occurrence prediction models for onion cultivation. The system integrates descriptive, exploratory, historical, and AGILE methodologies to develop a comprehensive mobile application. The effectiveness of OnionAider was evaluated through data retrieval, questionnaires, and interviews with farmers, experts, and relevant stakeholders. The results indicate a strong relationship between climatic data and pest infestation in onions, with temperature and humidity being the primary predictors. The developed mobile application incorporates an algorithm derived from the Proponent-Rete method and offers valuable features for onion farmers. The system complies with ISO 25010 software compliance criteria. This article discusses the methodology, results, and implications of OnionAider in enhancing onion cultivation practices.

A smart Sustainable decision Support system for water management oF power plants in water stress regions

Power Plants (PPs) is considered as critical facilities in each region because of essential role through energy generation processes. These facilities are also depended to water availability especially in water stress areas. Due to the critical water shortage in many areas around the world, it is necessary to make an optimal condition among water consumption and the increasing demand for electricity to prevent any further conflict of interests between industry, householders and the environmental goals. There are different techniques for controlling Water Consumption (WC) in these industries. This paper develops a smart Decision Support System (DSS) for monitoring, prediction and control sections based on Artificial Intelligent (AI) and integration of the PESTEL matrix and Multi Criteria Decision Making (MCDM) methods. Monitoring section comprises Fuel Consumption (FC), Atmospheric Temperature (AT), Power Plant Temperature (PPT) and Power Plant Efficiency (PPE), in which FC has the most influence on WC based on ANOVA evaluations in both cold and warm seasons. The prediction results have illustrated that Adaptive Neuro Fuzzy Inference System model is more efficient for the WC estimation with a correlation coefficient over 0.99. Ordered Weighted Averaging (OWA) also demonstrated that in the optimistic and pessimistic states, the most priority is linked to E3 (Establishment of evaporation control systems by contractor companies and concluding a guaranteed purchase contract with a power plant worth one and a half times the current amount of water price). In the last step of technical approaches, the smart controlling system is added for execution of water-energy nexus in the PP based on proportional–integral–derivative controller system. Finally, the performance of the DSS is approved with more than 80% agreement of experts and more than 90% precision in prediction procedure through this investigation. Application of this DSS can also be helpful for developing countries to achieve the UN Sustainable Development Goals.

Decision support system (DSS) for traffic prediction and building a dynamic internet community using Netnography technology in the city of Amman

ABSTRACT Recently, the increasing rapid number of cars on the roads and the great demand for traffic prediction, because of the increase in traffic congestion, has posed a great challenge to governments’ economic development and social stability of most countries in the world. While many challenges are facing governments to solve traffic congestion and reduce car accidents and pollution, the objective of this study is to apply a dynamic approach for the Jordanian community to lessen traffic congestion on the roads in Amman by utilising social media, artificial intelligence efficiency methods, and decision support to lessen traffic issues in the capital city of Jordan. It also aims to communicate directly with social media users to cut down on the time and effort needed to make traffic predictions, which will help to relieve the congestion of areas and roads in Amman. To obtain the research aims, we handled a TRF2021JOR dataset related to traffic records in Amman city, collected from Amman Municipality for several years. Then, we applied the Netnography model, which was built using the data science concepts, to create an efficient model in Amman city for traffic prediction and time series based on selective features of previous congestion in city roads. The experiment results showed the accuracy preference for the proposed method in Amman city. Furthermore, the experiment tested the accuracy of results based on the machine learning methods using the KNIME Analytics Platform tool, one of the Artificial Intelligence (AI) methods used; the experiment results showed that the classifier SVM gives a low accuracy reached (89.9%). However, the accuracy using the decision tree classifier reached (90.59%), while the classifier random forest gives a high accuracy reached (93.16%).