Agile project status prediction using interpretable machine learning

Ali Akbar Forouzesh Nejad; Farzad Arabikhan; Nigel Leroy Williams; Alexander Gegov; Omer Faruk Sari; Mohamed Bader-El-Den

Agile project status prediction using interpretable machine learning

Ali Akbar Forouzesh Nejad, Farzad Arabikhan, Nigel Leroy Williams, Alexander Gegov, Omer Faruk Sari, Mohamed Bader-El-Den

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Monitoring and forecasting the progress of information technology projects stands as a significant challenge in project management. Over the past two decades, agile project management has become a crucial factor influencing project success. Despite this, existing research has not presented a comprehensive model capable of predicting project outcomes based on agile features. In light of this, this study aims to develop a predictive model for information technology project outcomes using agility metrics. The results indicate that metrics related to teamwork and the team's capabilities, along with their collective experience, have the most significant impact on project success. The study employs the Decision Tree as an interpretable model to establish rules and predict project success. The accuracy of the model designed in this study is an impressive 97%, surpassing the accuracy of SVM at 71% and KNN at 82%.

Original language	English
Title of host publication	Proceedings of 2024 IEEE 12th International Conference on Intelligent Systems (IS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Publication status	Accepted for publication - 1 Jul 2024
Event	12th IEEE International Conference on Intelligent Systems - Varna, Bulgaria Duration: 29 Aug 2024 → 31 Aug 2024

Conference

Conference	12th IEEE International Conference on Intelligent Systems
Country/Territory	Bulgaria
City	Varna
Period	29/08/24 → 31/08/24

Keywords

Interpretable Machine Learning
Decision Tree
Agile Project Management
Project Status Prediction

Embargoed Document

Agile project status prediction using interpretable machine learning
Accepted author manuscript (Post-print), 690 KB
Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 1/01/50

Cite this

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title = "Agile project status prediction using interpretable machine learning",

abstract = "Monitoring and forecasting the progress of information technology projects stands as a significant challenge in project management. Over the past two decades, agile project management has become a crucial factor influencing project success. Despite this, existing research has not presented a comprehensive model capable of predicting project outcomes based on agile features. In light of this, this study aims to develop a predictive model for information technology project outcomes using agility metrics. The results indicate that metrics related to teamwork and the team's capabilities, along with their collective experience, have the most significant impact on project success. The study employs the Decision Tree as an interpretable model to establish rules and predict project success. The accuracy of the model designed in this study is an impressive 97%, surpassing the accuracy of SVM at 71% and KNN at 82%.",

keywords = "Interpretable Machine Learning, Decision Tree, Agile Project Management, Project Status Prediction",

author = "{Forouzesh Nejad}, {Ali Akbar} and Farzad Arabikhan and Williams, {Nigel Leroy} and Alexander Gegov and Sari, {Omer Faruk} and Mohamed Bader-El-Den",

note = "No embargo - IEEE {\textcopyright} {\textcopyright} 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 12th IEEE International Conference on Intelligent Systems ; Conference date: 29-08-2024 Through 31-08-2024",

year = "2024",

month = jul,

day = "1",

language = "English",

booktitle = "Proceedings of 2024 IEEE 12th International Conference on Intelligent Systems (IS)",

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address = "United States",

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Forouzesh Nejad, AA , Arabikhan, F , Williams, NL , Gegov, A, Sari, OF & Bader-El-Den, M 2024, Agile project status prediction using interpretable machine learning. in Proceedings of 2024 IEEE 12th International Conference on Intelligent Systems (IS). Institute of Electrical and Electronics Engineers Inc., 12th IEEE International Conference on Intelligent Systems, Varna, Bulgaria, 29/08/24.

Agile project status prediction using interpretable machine learning. / Forouzesh Nejad, Ali Akbar ; Arabikhan, Farzad ; Williams, Nigel Leroy et al.
Proceedings of 2024 IEEE 12th International Conference on Intelligent Systems (IS). Institute of Electrical and Electronics Engineers Inc., 2024.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Williams, Nigel Leroy

AU - Gegov, Alexander

AU - Sari, Omer Faruk

AU - Bader-El-Den, Mohamed

N1 - No embargo - IEEE © © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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N2 - Monitoring and forecasting the progress of information technology projects stands as a significant challenge in project management. Over the past two decades, agile project management has become a crucial factor influencing project success. Despite this, existing research has not presented a comprehensive model capable of predicting project outcomes based on agile features. In light of this, this study aims to develop a predictive model for information technology project outcomes using agility metrics. The results indicate that metrics related to teamwork and the team's capabilities, along with their collective experience, have the most significant impact on project success. The study employs the Decision Tree as an interpretable model to establish rules and predict project success. The accuracy of the model designed in this study is an impressive 97%, surpassing the accuracy of SVM at 71% and KNN at 82%.

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