Decision System Research Articles

Exploring Trade-offs in Explainable AI

Machine Learning (ML) models are increasingly used in systems that involve physical human interaction or decision-making systems that impact human health and safety. Ensuring that these systems are safe and reliable is an important topic of current AI research. For many ML models it is unclear how a prediction (output) is arrived at from the provided features (input). Critical systems cannot blindly trust the predictions of such "black box" models, but instead need additional reassurance via insight into the model's reasoning. A range of methods exist within the field of Explainable AI (XAI) to make the reasoning of black box ML models more understandable and transparent. The explanations provided by XAI methods may be evaluated in a number of (competing) ways. In this paper, we investigate the trade-off between selected metrics for an XAI method called UnRAvEL, which is similar to the popular LIME approach. Our results show that by weighting the terms within the acquisition function used in UnRAvEL, different trade-offs can be achieved.

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part 2: Applications

To address the challenge of interpreting the decisions made by machine learning-based control algorithms, the Integrated Artificial Reasoning Framework (IARF) introduced in Part I of this work was developed. Further implementing this framework within a Markov Decision Process (MDP) yielded a decision-making system which produces optimal decisions whose logic can be easily traced and understood. The IARF with MDP formulated in Part I of this study was applied to two case studies of relevance to the nuclear power community: 1) operational decision-making while experiencing the degradation of key components, and 2) optimal control of a nuclear power and hydrogen co-generation facility. This work details the steps of implementing and analyzing these two studies, beginning with the creation of simulation models using the Modelica programming language, covering the formulation of the MDP using the IARF, and concluding with the execution and analysis of the scenarios. Technical challenges related to implementation, such as maintaining physically understandable traceability and efficiently producing large amount of data for calculating state transition probabilities, are addressed and their resolutions are suggested. Emphasis is placed on the benefits of relying on a replaceable modeling framework for simulating system behavior. Results indicate that optimal operational trajectories were successfully found by the MDP, and through utilization of the IARF, these results are easily interpretable and understandable to any user.

Time pressure increases children's aversion to advantageous inequity.

The relative contribution of intuitive and reflective cognitive systems in cooperative decision making is a topic of hot debate. Research with adults suggests that intuition often favors cooperation, but these effects are contextually sensitive. Emerging evidence has shown that in many contexts children show a tendency toward intuitive cooperation, but research investigating these processes in children is sparse and has produced mixed findings. In the current study we investigated the influence of intuitive and reflective decision processes on children's fairness behavior by manipulating decision time. We tested (N = 158) pairs of children between 4 and 10 years of age from a rural community in Canada. Children's decisions to accept or reject allocations of candies were either made under time pressure or after a 10-s delay. We assessed the impact of decision time on children's aversion to inequitable distributions of resources by comparing their responses to equal allocations with either disadvantageous allocations or advantageous allocations. We found that children showed a greater age-related increase in advantageous inequity aversion when decisions were made under time pressure compared to when they were made after a delay. In contrast, we did not observe a significant impact of decision time on the development of disadvantageous inequity aversion. These findings suggest that intuitive decision processes may contribute to the development of fairness concerns in middle childhood.

Prediction model of early recurrence of multimodal hepatocellular carcinoma with tensor fusion

Objective. In oncology, clinical decision-making relies on a multitude of data modalities, including histopathological, radiological, and clinical factors. Despite the emergence of computer-aided multimodal decision-making systems for predicting hepatocellular carcinoma (HCC) recurrence post-hepatectomy, existing models often employ simplistic feature-level concatenation, leading to redundancy and suboptimal performance. Moreover, these models frequently lack effective integration with clinically relevant data and encounter challenges in integrating diverse scales and dimensions, as well as incorporating the liver background, which holds clinical significance but has been previously overlooked. Approach. To address these limitations, we propose two approaches. Firstly, we introduce the tensor fusion method to our model, which offers distinct advantages in handling multi-scale and multi-dimensional data fusion, potentially enhancing overall performance. Secondly, we pioneer the consideration of the liver background’s impact, integrating it into the feature extraction process using a deep learning segmentation-based algorithm. This innovative inclusion aligns the model more closely with real-world clinical scenarios, as the liver background may contain crucial information related to postoperative recurrence. Main results. We collected radiomics (MRI) and histopathological images from 176 cases diagnosed by experienced clinicians across two independent centers. Our proposed network underwent training and 5-fold cross-validation on this dataset before validation on an external test dataset comprising 40 cases. Ultimately, our model demonstrated outstanding performance in predicting early recurrence of HCC postoperatively, achieving an AUC of 0.883. Significance. These findings signify significant progress in addressing challenges related to multimodal data fusion and hold promise for more accurate clinical outcome predictions. In this study, we exploited global 3D liver background into modelling which is crucial to to the prognosis assessment and analyzed the whole liver background in addition to the tumor region. Both MRI images and histopathological images of HCC were fused at high-dimensional feature space using tensor techniques to solve cross-scale data integration issue.

THE METHOD OF RANKING EFFECTIVE PROJECT SOLUTIONS IN CONDITIONS OF INCOMPLETE CERTAINTY

The subject of research in the article is the process of ranking options in support systems for project decision-making under conditions of incomplete certainty. The goal of the work is to increase the efficiency of technologies for automated design of complex systems due to the development of a combined method of ranking effective options for building objects in conditions of incomplete certainty of input data. The following tasks are solved in the article: analysis of the current state of the problem of ranking options in support systems for project decision-making; decomposition of problems of system optimization of complex design objects and support of project decision-making; development of a variant ranking method that combines the procedures of lexicographic optimization and cardinal ordering in conditions of incomplete certainty of input data. The following methods are used: systems theory, utility theory, optimization, operations research, interval and fuzzy mathematics. Results. According to the results of the analysis of the problem of supporting project decision-making, the existence of the problem of correctly reducing subsets of effective options for ranking, taking into account factors that are difficult to formalize and the experience of the decision-maker (DM), was established. Decomposition of the problems of system optimization of complex design objects and support for project decision-making was carried out. For the case of ordinalistic presentation of preferences between local criteria, an estimate of the size of the rational reduction of subsets of optimal and suboptimal options for each of the indicators is proposed. Its use allows for one approach to obtain a subset of effective variants of a given capacity for analysis and final selection of the DM. A method of transforming the ordinalistic presentation of preferences between local criteria to their quantitative presentation in the form of weighting coefficients is proposed. Conclusions. The developed methods expand the methodological foundations of the automation of processes supporting the adoption of multi-criteria project decisions. They make it possible to correctly reduce the set of effective alternatives in conditions of incomplete certainty of the input data for the final choice, taking into account factors that are difficult to formalize, knowledge and experience of ODA. The practical use of the obtained results will allow to reduce the time and capacity complexity of the procedures for supporting project decision-making, and due to the use of the technology of selection of subsets of effective options with intervally specified characteristics - to guarantee the quality of project decisions and to provide a more complete assessment of them.

INTELLIGENT INFORMATION TECHNOLOGIES TO SUPPORT DECISION-MAKING WHEN APPLYING THE CAD/CAM/CAE SYSTEM OF DESIGN AND USING ADDITIVE TECHNOLOGIES

The direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. The relevance is due to the fact that the development of an intelligent decision-making support system based on neural network modeling, the development of methods for their training, tabagato-criterion optimization of design processes, will allow the creation of three-dimensional solid models of defects taking into account their spatial structure and bone substitutes for the synthesis of biomaterials with controlled composition, porosity and mechanical strength, which are optimal for a specific area of bone replacement, which will increase the effectiveness of treatment and prosthetics in orthopedics and traumatology. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization, and training is proposed. A modification of the method of learning neural networks based on gradient descent, based on the application of the theory of nonlinear dynamics, is proposed. Corresponding theoretical provisions have been developed.

Application of intelligent decision system based on artificial neural network in free forging of the engine main shaft

Application of intelligent decision system based on artificial neural network in free forging of the engine main shaft

Earthquake prediction using satellite data: Advances and ahead challenges

Due to the considerable loss of life and damages caused by powerful Earthquakes, along with the strengthening of structures and disaster management systems, many researches are being conducted associated with the possibility of creating Earthquake warning systems. However, no reliable scientific report on successful Earthquake prediction has been published so far. Although there are a couple of published scientific papers indicating the detection of unusual changes in physical and chemical parameters obtained from ground stations, but due to the limitations of in-situ measurement in terms of number, location, time and cost, there was no noticeable progress in Earthquake warning. After the advent of remote sensing satellites, the number of statistical studies related to Earthquake precursors increased significantly, and most of them emphasized the existence of abnormal behaviors in physical and chemical precursors around the time (about 1–30 days before) and locations of powerful Earthquakes. In this study, an attempt has been made to address the advances and challenges in the way of Earthquake prediction using satellite data and provide a perspective of the future of these researches. It should be noted that the increase in the number of satellites designed and launched specially for Earthquake studies, the variety of available Earthquake precursors (multi-precursor analysis), the development of classic and intelligent anomaly detection and predictor algorithms (multi-method analysis), the provision of intelligent systems for the integration of various precursors (fusion and decision systems), the creation of cloud storing and processing data services (Google Earth Engine, Giovanni, etc.) and the development of intelligent user interfaces for public, have made researchers more hopeful for the appearance of low-uncertainty Earthquake warning systems in the near future.

Optimizing Digital Financial Data: Enhancing Business Decision Making in the Indian Scenario

The study aims to explore challenges hindering inclusive access to digital financial inclusion (DFI) in the Indian financial sector, focusing on the role of automation in financial decision-making support systems. It also seeks to assess the impact of digital technologies on corporate financial performance and Environmental, Social, and Governance (ESG) considerations. Descriptive research methodology was conducted to analyse challenges affecting inclusive access to DFI, with a sample size of 388 participants from major financial institutions in Mysore city. Primary data was collected through structured questionnaires, and secondary data was obtained from relevant literature and reports. Statistical methods including ANOVA were employed for data analysis. The analysis revealed non-significant differences in digital literacy programs and technological infrastructure among banks, highlighting a uniform approach and the need for standardized development. Significant variation was observed in gender-based challenges, emphasizing the necessity for tailored initiatives. Non-significant differences in automation adoption and system usage imply a consistent approach across banks, though further investigation is warranted. Borderline significance in stakeholder perception suggests diverse opinions, requiring proactive management during digital transformation. The study underscores the importance evaluations and collective efforts to address challenges hindering inclusive access to digital financial services in India. While certain aspects exhibit consistency, targeted interventions are needed to foster inclusivity and maximize the potential of digital technologies. Future research should explore stakeholder perceptions and influencing factors behind automation adoption trends to enhance understanding of digital financial inclusion dynamics in India.

An improved method planning path of an autonomous ground robot with using the MBD-RRT*FFT algorithm

The study analyzed the problems associated with planning the path of robots and increasing the accuracy and reliability of their guidance in real time. To solve this problem, a practical study was conducted, based on the obtained analysis, a modification of the asymptotically optimal BD-RRT*FT algorithm and was proposed and improved the MBD-RRT*FFT algorithm. was developed. Which, when applied in dynamic environments due to the use of multi-threaded computations, has better dynamic scheduling capabilities, and the use of Fitch's optimal search result selection algorithm provides a general tendency to choose the optimal straight-line path. To verify the effectiveness of the proposed algorithm improvement method, simulations were carried out using our own simulation program. A comparison of the MBD-RRT*FFT algorithm with other algorithms was carried out on three maps with an evaluation of performance indicators. In order to evaluate the behavior of the program, as well as to identify bottlenecks in the execution of the algorithm, the memory load of the decision-making system was monitored at all stages of the verification. This made it possible to see an increase in the consumption of memory resources by 12-15%, but taking into account the obtained results regarding the optimality and speed of calculations, conclusions were made regarding the minimal impact. Additional understanding of the need for careful selection of the hardware component of decision-making systems is provided. The scientific novelty of the method consists in the application of a scientific and methodological apparatus for improving the BD-RRT*FT algorithm, which ensured further development in terms of expanding and supplementing known data on algorithms with robot path planning and increasing accuracy and reliability in real time. The method includes methods of research, systematization and adjustment of new and previously acquired knowledge.

Innovative Technology for Managing Biofuel Production from Timber Industry Waste

The relevance of this study is determined by the growing worldwide interest in renewable energy sources against the backdrop of depleting fossil fuel reserves. This study aims to develop an innovative technology for managing biofuel production from wood waste, including a set of interrelated economic and mathematical models focused on maximizing the fuel and energy efficiency of biofuels depending on the location of waste generation, feedstock moisture content, and distance to the biofuel production site. This technology should also combine the main directions of international research in the field of environmental responsibility of countries in terms of carbon dioxide (CO2) emissions and the Paris Climate Agreement. The methodological basis of the research comprises the authors’ innovative technology based on a set of interconnected economic and mathematical models and managerial decision-making systems, methods for nonlinear programming, system analysis, an information approach to the analysis of systems, accepted technological processes, norms, and standards established in the international practice of the timber industry. This innovative technology was implemented in practice using the capabilities of the MathCad and MS Excel software products. The article determines the optimal operating parameters of timber industry enterprises at which the specific thermal energy of the produced biofuel exceeds by at least 15% the thermal energy spent on processing this biofuel as an energy carrier. Wood waste biofuel production is profitable if the distance for feedstock transportation to the production site does not exceed 80 km and the relative humidity of the raw materials does not exceed 60%. Doi: 10.28991/ESJ-2024-08-03-03 Full Text: PDF

Potential of hyperspectral imaging for nondestructive determination of α-farnesene and conjugated trienol content in ‘Yali’ pear

The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in ‘Yali’ pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, Rp = 0.925 and RMSEP = 4.387 nmol cm−2. And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, Rp = 0.913 and RMSEP = 2.734 nmol cm−2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in ‘Yali’ pear, which would be helpful for supporting postharvest decision systems.

Expression of Concern: Water Supply 21 (8), 4145–4152: Application of probability decision system and particle swarm optimization for improving soil moisture content, Panpan Zhu, Hossein Saadati and Majid Khayatnezhad, https://dx.doi.org/10.2166/ws.2021.169

Expression of Concern: <i>Water Supply</i> 21 (8), 4145–4152: Application of probability decision system and particle swarm optimization for improving soil moisture content, Panpan Zhu, Hossein Saadati and Majid Khayatnezhad, https://dx.doi.org/10.2166/ws.2021.169

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy

Objective. To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model. Approach. Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds. Main results. Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively. Significance. Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

A Cross-Stage Partial Network and a Cross-Attention-Based Transformer for an Electrocardiogram-Based Cardiovascular Disease Decision System.

Cardiovascular disease (CVD) is one of the leading causes of death globally. Currently, clinical diagnosis of CVD primarily relies on electrocardiograms (ECG), which are relatively easier to identify compared to other diagnostic methods. However, ensuring the accuracy of ECG readings requires specialized training for healthcare professionals. Therefore, developing a CVD diagnostic system based on ECGs can provide preliminary diagnostic results, effectively reducing the workload of healthcare staff and enhancing the accuracy of CVD diagnosis. In this study, a deep neural network with a cross-stage partial network and a cross-attention-based transformer is used to develop an ECG-based CVD decision system. To accurately represent the characteristics of ECG, the cross-stage partial network is employed to extract embedding features. This network can effectively capture and leverage partial information from different stages, enhancing the feature extraction process. To effectively distill the embedding features, a cross-attention-based transformer model, known for its robust scalability that enables it to process data sequences with different lengths and complexities, is employed to extract meaningful embedding features, resulting in more accurate outcomes. The experimental results showed that the challenge scoring metric of the proposed approach is 0.6112, which outperforms others. Therefore, the proposed ECG-based CVD decision system is useful for clinical diagnosis.

Survival benefit of postoperative radioiodine therapy among patients with intermediate-risk differentiated thyroid carcinoma.

The 2015 American Thyroid Association (ATA) guidelines proposed the use of the ATA Risk Stratification System and American Joint Committee on Cancer Tumor-Node-Metastasis (AJCC/TNM) Staging System for postoperative radioiodine decision-making. However, the management of patients with intermediate-risk differentiated thyroid carcinoma (DTC) is not well defined. In this study, we aimed to evaluate the therapeutic efficacy of radioactive iodine therapy (RAIT) among various subgroups of patients with intermediate-risk DTC after surgery. This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015). The DTC patients with intermediate risk of recurrence were divided into two groups (treated or not treated with radioactive iodine (RAI)). As the treatment was not randomly assigned, stabilized inverse probability treatment weighting (sIPTW) was used to reduce selection bias. We used the Kaplan-Meier method and log-rank test to analyze overall survival (OS) and cancer-specific survival (CSS). Kaplan-Meier analysis after sIPTW found a significant difference in OS and CSS between no RAIT and RAIT (log-rank test, P < 0.0001; P = 0.0019, respectively). The Kaplan-Meier curves of CSS in age cutoff of 55 years showed a significant association between no RAIT and RAIT (log-rank test, P = 0.0045). Univariate and multivariate Cox regression showed RAIT was associated with a reduced risk of mortality compared with no RAIT (hazard ratio [HR] 0.59, 95% confidence interval [95% CI 0.44-0.80]). Age (≥ 55) years showed a worse CSS regardless of whether or not a patient was treated or not treated with RAI ([HR] 8.91, 95% confidence interval [95% CI 6.19-12.84]). RAIT improves OS and CSS in patients with intermediate-risk DTC after surgery. 55 years is a more appropriate prognostic age cutoff for the relevant classification systems and is a crucial consideration in RAI decision-making. Therefore, we need individualized treatment plans.

THE USE OF EMPIRICAL ANTIBIOTICS IN THE CRITICAL CARE UNIT

Antimicrobial therapy plays a crucial role in managing infections in critically ill patients, yet its indiscriminate use can lead to adverse outcomes. Here, we propose a systematic approach for guiding empirical antimicrobial therapy in intensive care unit (ICU) patients with suspected infections. Diagnostic evaluation, including imaging, laboratory tests, and physical examination, is essential to assess the necessity of antimicrobial treatment. Despite advances, no gold standard biomarker for differentiating infection from inflammation necessitates thorough diagnostic assessment. Prompt microbiological sampling is crucial for accurate diagnosis and tailored treatment. Emergency initiation of antimicrobial therapy is recommended for conditions like sepsis and septic shock, emphasizing the need for timely intervention. Pathogen profiling and consideration of multidrug resistance risk factors are essential for selecting appropriate empirical therapy. Customized evaluation based on patient characteristics and regional trends is paramount for optimal antibiotic selection. Pharmacokinetic and pharmacodynamic parameters should guide dosing to ensure adequate antibacterial levels while minimizing adverse effects. Future research should focus on AI-powered decision-making systems, optimal timelines for starting or postponing therapy, and the environmental impacts of antibiotic use to enhance patient outcomes and reduce antimicrobial resistance.

Development of data-knowledge-driven predictive model and multi-objective optimization for intelligent optimal control of aluminum electrolysis process

Operational optimization of the Hall-Héroult cell is essential for achieving high efficiency and cost-effectiveness in the aluminum electrolysis process. Due to the complicated mechanism and variable working conditions, manual operational decision-making is extensively used in practice. They challenge the reliable and optimal operation of aluminum electrolysis process. In this paper, we develop a data-knowledge-driven decision-making support system (DMSS) to achieve operational optimization for the aluminum electrolysis process. DMSS consists of a prediction model, a multi-objective optimizer, and a knowledge-guided decision-making module. Specifically, we propose a working-conditions-based attention with the exogeneous inputs auto-regressive neural network (WCA-NARX) to construct a data-driven heat balance indicator (HBI) prediction model, where the working condition-related variables serve as covariates to enhance predictability. In addition, the designed structure of introducing working condition information through an attention mechanism can decouple covariates from operational variables and autoregressive variables, facilitating subsequent operational optimization. Then, a novel knowledge-assigned reference vector evolutionary algorithm (KRVEA) is designed to solve the multi-objective optimization problem of the aluminum electrolysis process, in which Pareto front solutions can be solved in the preferred region. Finally, we utilize the knowledge base that stores historical optimization cases to make decisions regarding the selection of a practical-requirement-based control scheme from the Pareto set. Real-world industrial experiments demonstrate that DMSS can effectively enhance control performance and achieve superior results compared to other competitive methods. The source code is available at https://github.com/wjiecsu/WCA-NARX.

Occupational Health Barriers in South Africa: A Call for Ubuntu.

Many low- and middle-income countries (LMICs) grapple with shortages of health workers, a crucial component of robust health systems. The COVID-19 pandemic underscored the imperative for appropriate staffing of health systems and the occupational health (OH) threats to health workers. Issues related to accessibility, coverage, and utilization of OH services in public sector health facilities within LMICs were particularly accentuated during the pandemic. This paper draws on the observations and experiences of researchers engaged in an international collaboration to consider how the South African concept of Ubuntu provides a promising way to understand and address the challenges encountered in establishing and sustaining OH services in public sector health facilities. Throughout the COVID-19 pandemic, the collaborators actively participated in implementing and studying OH and infection prevention and control measures for health workers in South Africa and internationally as part of the World Health Organizations' Collaborating Centres for Occupational Health. The study identified obstacles in establishing, providing, maintaining and sustaining such measures during the pandemic. These challenges were attributed to lack of leadership/stewardship, inadequate use of intelligence systems for decision-making, ineffective health and safety committees, inactive trade unions, and the strain on occupational health professionals who were incapacitated and overworked. These shortcomings are, in part, linked to the absence of the Ubuntu philosophy in implementation and sustenance of OH services in LMICs.

Formulating the Primacies of Vaccination Against COVID-19 Among Individuals Utilising the Analytic Hierarchy Process

Early detection of any disease is half cured but the vaccination process is likewise vital for the control of spread of diseases. Great efforts and initiatives are being made by researchers for the development of COVID-19 vaccines. Looking at the current scenario, the demand of COVID-19 vaccine surpasses the supply. It is very important to formulate the priorities of individuals for the vaccination drive at various stages. In this article, analytic hierarchy process is applied to develop a decision-making system to decide the priority of an individual to be vaccinated depending upon the unique features they might possess. The various characteristics are decided based on research and consultation from academicians, peer researchers and medical professionals. This study supports the prioritisation of vaccination to individuals with low immunity levels, which is followed by the presence of comorbidity, then followed by occupation, age and then area of their stay. This article may help countries to set up the priorities during their various stages’ vaccination drive. Countries that have not yet started with the vaccination process may follow the mentioned priorities in various stages.