Survey on Different Data Mining Techniques for Prediction

Akulwar, Pooja; Pardeshi, Sujata; Kamble, Arvind

doi:10.1109/i-smac.2018.8653734

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…Survival analysis involves analyzing data in which the outcome variable is the time until a specific event occurs. Proportional hazard regression is the most widely used method for studying how predictor variables impact survival time [ 11 ]. Logistic regression classifies observations using a model and probability estimates, which is effective for categorical data [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

“…Two main types of computer learning systems are supervised and unsupervised, which have greatly improved the decision-making process [ 10 ]. Data analytics incorporates numerous data mining techniques, such as association, classification, clustering, prediction, sequential patterns, and decision tree [ 11 ]. A classification technique is utilized in machine learning to predict output based on a pre-determined set of classes or groups.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients

Alanazi,

Aldakhil,

Aldhoayan

et al. 2023

Medicina

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Background and Objectives: Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital signs of adult patients in the ICU, this approach can predict and detect the initial signs of sepsis. Materials and Methods: To examine survival rates and predict outcomes, the study utilized several models, including the proportional hazards model and data mining algorithms. We analyzed data from the BESTCare database at KAMC, with a focus on patients aged 14 and older who were admitted to the ICU between April and October 2018. We conducted a thorough analysis of the medical records of a total of 1182 patients who were diagnosed with sepsis. Results: We studied two approaches to predict sepsis in ICU patients. The regression model utilizing survival analysis showed moderate predictive ability, emphasizing the importance of only three factors—time (from sepsis to an outcome; discharge or death), lactic acid, and temperature—had a significant p-value (p = 0.000568, p = 0.01, p = 0.02, respectively). Other data mining algorithms may have limitations due to their assumptions of variable independence and linear classification nature. Conclusions: To achieve progress and accuracy in the field of sepsis prediction, it is important to continuously strive for improvement. By meticulously cleaning and selecting data attributes, we can create a strong foundation for future advancements in this area.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients

Alanazi,

Aldakhil,

Aldhoayan

et al. 2023

Medicina

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show abstract

“…In its simplest versions, decision trees are straightforward algorithms that are easy to observe and understand. These models, however, could be excessively straightforward for problems with more intricate elements [31]. Numerous treebased methods were created to increase accurateness and maintain dispensation competence.…”

Section: Decision Trees (Dt)mentioning

confidence: 99%

Intelligent Fault Diagnosis of Manufacturing Processes Using Extra Tree Classification Algorithm and Feature Selection Strategies

Yousefi,

Yin,

Alfarizi

2023

IEEE Open J. Ind. Electron. Soc.

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Fault diagnosis is integral to maintenance practices, ensuring optimal machinery functionality. While traditional methods relied on human expertise, Intelligent Fault Diagnosis (IFD) techniques, propelled by Machine Learning (ML) advancements, now offer automated fault identification. Despite their efficiency, a research gap exists, emphasizing the need for methods providing not just reliable fault identification but also in-depth causal factor analysis. This research introduces a novel approach using an Extra Tree classification algorithm and feature selection to identify fault importance in manufacturing processes. Compared with SVM, neural networks, and tree-based ML, the method enhances training and computational efficiency, achieving over 99% classification accuracy on PHM 2021 dataset. Importantly, the algorithm enables researchers to analyze individual fault causes, addressing a critical research gap. The study provides guidelines for further research, aiming to refine the proposed strategy. This work contributes to advancing fault diagnosis methodologies, combining automation with comprehensive causal analysis, crucial for both academic and industrial applications.

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“…According to [5], [6], Education Data Mining (EDM) is referred to as the development of methods and models that can be used to extract useful information from data derived from the educational environment. Student academic performance in university should be a big concern not only for the students and parents but also for the university administration and faculty.…”

Section: Introductionmentioning

confidence: 99%

Quality enhancement at higher education institutions by early identifying students at risk using data mining

Mahboob

Asif

Haider

2023

Mehran Univ. res. j. eng. technol.

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Accurate prediction of students' academic performance is one of the challenges in maintaining quality standards in any Higher Education Institution (H.E.I.). To ensure the quality of teaching and learning, H.E.I.s often employ Self-Assessment Reports (S.A.R.s) in which identifying a student drop-out ratio is important. Hence, it is essential to identify at-risk students in a given academic program. This article aims to identify at-risk students early by proposing a data mining-based predictive framework to improve the student's learning experience and minimize the dropped-out ratio. The academic sub-attributes or indicators in each course that may affect the performance of students in higher education institutions used in this study to examine students' academic achievement and predict students' performance to distinguish at-risk students are the marks of assignments, mid-term, lab exams, semester marks, total, grade, grade point (G.P.), quality point (Q.P.), grade point average (G.P.A.), and credit hours data of multiple courses categorized according to three knowledge areas defined by Higher Education Commission (H.E.C), Pakistan using data mining predictive techniques. The results indicate that the proposed methods can achieve maximum accuracy in predicting and identifying at-risk students in different courses.

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Survey on Different Data Mining Techniques for Prediction

Cited by 9 publications

References 7 publications

Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients

Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients

Intelligent Fault Diagnosis of Manufacturing Processes Using Extra Tree Classification Algorithm and Feature Selection Strategies

Quality enhancement at higher education institutions by early identifying students at risk using data mining

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