Decision Support System to Risk Stratification in the Acute Coronary Syndrome Using Fuzzy Logic

Xi, Hui; Nazir, Shah; Li, Han-ming; Khan, Habib Ullah; Li, Chengwei

doi:10.1155/2021/6571905

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…However, despite the promising prospects of AI, challenges persist. Inaccuracies in sales predictions and the escalating complexity of analyzing vast datasets pose ongoing obstacles [7]- [12]. the studies discussed earlier shed light on the pivotal role of AI in stocking and warehouse management, contributing to advancements in industrial decision-making.…”

Section: Introductionmentioning

confidence: 99%

Inventory Optimization Using an AI-Powered Holistic Model for Decision-Support System

Choudhary

2024

IJRASET

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This study presents a groundbreaking approach to inventory optimization through the implementation of an AIpowered holistic model within a decision-support system. Leveraging machine learning algorithms, specifically Logistic Regression (LR) and Decision Trees, the methodology explores historical sales data, supplier metrics, and market trends during an extensive Exploratory Data Analysis phase. The implementation of an AI-powered holistic model for inventory optimization in a decision-support system presents a transformative approach to managing and enhancing supply chain efficiency. The proposed methodology, integrating machine learning algorithms such as Logistic Regression and Decision Trees, has demonstrated its efficacy in achieving superior results compared to traditional models, as evidenced by higher accuracy, precision, recall, and F1 scores. The comprehensive exploration of historical sales data, supplier metrics, and market trends during the exploratory data analysis phase has facilitated a nuanced understanding of inventory dynamics. The seamless integration of the AI-powered model into the decision-support system has empowered organizations with timely and data-driven insights, fostering more agile and informed decision-making. The seamless integration of the AI-powered model into the decision-support system provides organizations with timely and data-driven insights, fostering agile and informed decisionmaking. Performance evaluation, including accuracy, precision, recall, and F1 scores, reveals that the proposed LR and Decision Tree models outperform the existing Support Vector Machine (SVM) model across all metrics. The LR model exhibits commendable precision, recall, and F1 score values of 0.95, 0.97, and 0.98, respectively, while the Decision Tree model demonstrates even higher values, with precision and recall at 0.96 and an exceptionally high F1 score of 0.99. These outcomes underscore the practical utility and robustness of the AI-powered holistic model in revolutionizing inventory management practices. While acknowledging challenges and limitations, this research signifies a crucial advancement in establishing responsive and intelligent decision-support systems for inventory optimization, paving the way for future innovations in supply chain management

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Section: Introductionmentioning

confidence: 99%

Inventory Optimization Using an AI-Powered Holistic Model for Decision-Support System

Choudhary

2024

IJRASET

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“…Myocardial infarction, or a heart attack, is one such disorder that occurs when cell death causes damage to or destruction of heart tissue. Reduced blood flow to the heart is a warning indicator indicating an increased risk of heart attack [1], even if acute coronary syndrome does not directly cause cell death.…”

Section: Introductionmentioning

confidence: 99%

Risk stratification of covid-19 patients presented with acute coronary syndrome

A.Muhumed

A.Elkhalek²,

A.Mostafa³

et al. 2022

Benha Journal of Applied Sciences

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Multiple cases of cardiovascular involvement, including acute coronary syndrome, have been recorded since the catastrophic epidemic of coronavirus disease 2019 (COVID-19) in early 2020. The purpose of this investigation was to categorise people with acute coronary syndrome (ACS) into one of nineteen different groups. Patients and Methods: From May 2021 to April 2022, 200 patients with ACS were hospitalised to the CCU Department at Benha University Hospital for this prospective research. The GRACE scores of the groups investigated do not vary substantially from one another (non-significantly higher in patients with The results of this research show that people with ACS who also have COVID-19 are more likely to appear late and to die while hospitalised than those with ACS who do not have COVID-19.

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“…Hooda has designed a fuzzy algorithm that shows improvements on the existing disk scheduling algorithm for reduced seek time and rotational delay [20]- [21]. S. Nazir et.al have used Fuzzy Logic in 'Software Component Selection', 'User Knowledge', 'Data Modelling', 'Visualizations', 'Decision Support System for Component Security Evaluation' and ,and 'Risk Stratification in the Acute Coronary Syndrome' [29]- [32]. This proves that fuzzy logic has an incredible amount of potential, and it does impact the performance of the system.…”

mentioning

confidence: 99%

Designing Fuzzy Logic Controller for UNIX find Command

Arif Butt,

Idrees,

Saleem Raza

et al. 2022

VFAST trans. softw. eng.

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This research article propose a novel fuzzy logic controller (FLC) for fuzzification of the standard UNIX find(1) command. The interface of the proposed ffind(1) command is more user friendly and efficiently handles imprecise conditions. The FLC of the novel fuzzy-find (ffind(1)) shell command is written in C language, and it can locate files and directories using three associated timestamps (mtime, ctime, and atime). The user specifies the search criteria which can be any of the three times in days, the ffind(1) command recursively reads the timestamps from inode blocks of all the files under the given directory hierarchy. The ffind(1) command converts the file timestamps (number of seconds passed since UNIX epoch) into days, and then the difference between user given value and each of the file’s timestamp is calculated. These crisp differences of each file are input to our proposed fuzzy logic controller, which finally generates a crisp selection index (S.I.) value for each file. The file having the maximum S.I. value is the target file. Moreover, the user can also give a threshold value to see the sorted list of files that are a close match to the given selection criteria. The proposed study designed an algorithm that demonstrates the complete working of ffind(1) command and also compare the standard UNIX find(1) command with the novel ffind(1) command using different data sets. The results show that the proposed ffind(1) command performs significantly better than the vanilla flavour find(1) command. The proposed ffind(1) command once added in the /bin directory of any UNIX based system will allow the users of the system to make use of the more powerful and flexible features of ffind(1) in order to search for files and directories in a more user friendly or fuzzy way, which is the way humans thinks.

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Decision Support System to Risk Stratification in the Acute Coronary Syndrome Using Fuzzy Logic

Cited by 5 publications

References 42 publications

Inventory Optimization Using an AI-Powered Holistic Model for Decision-Support System

Inventory Optimization Using an AI-Powered Holistic Model for Decision-Support System

Risk stratification of covid-19 patients presented with acute coronary syndrome

Designing Fuzzy Logic Controller for UNIX find Command

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