A Systematic Literature Review on Applying CRISP-DM Process Model

Schröer, Christoph; Kruse, Felix; Gómez, Jorge Marx

doi:10.1016/j.procs.2021.01.199

Cited by 306 publications

(139 citation statements)

References 23 publications

Supporting

Mentioning

101

Contrasting

Unclassified

Order By: Relevance

“…CRISP-DM (cross-industry standard process for data mining) is one of the most established methodologies to apply data mining tasks [26]. In our approach existing methodologies were studied and adopted as guidelines, with CRISP-DM playing a major role in this procedure, to build a custom methodology consisting of a series of processes, each one focused on a particular task.…”

Section: Methodsmentioning

confidence: 99%

Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games

Karapapas

Goumopoulos

2021

Applied Sciences

View full text Add to dashboard Cite

Mild cognitive impairment (MCI) is an indicative precursor of Alzheimer’s disease and its early detection is critical to restrain further cognitive deterioration through preventive measures. In this context, the capacity of serious games combined with machine learning for MCI detection is examined. In particular, a custom methodology is proposed, which consists of a series of steps to train and evaluate classification models that could discriminate healthy from cognitive impaired individuals on the basis of game performance and other subjective data. Such data were collected during a pilot evaluation study of a gaming platform, called COGNIPLAT, with 10 seniors. An exploratory analysis of the data is performed to assess feature selection, model overfitting, optimization techniques and classification performance using several machine learning algorithms and standard evaluation metrics. A production level model is also trained to deal with the issue of data leakage while delivering a high detection performance (92.14% accuracy, 93.4% sensitivity and 90% specificity) based on the Gaussian Naive Bayes classifier. This preliminary study provides initial evidence that serious games combined with machine learning methods could potentially serve as a complementary or an alternative tool to the traditional cognitive screening processes.

show abstract

Section: Methodsmentioning

confidence: 99%

Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games

Karapapas

Goumopoulos

2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…For planning and execution, this study used the Cross-Industry Standard Process-Data Mining (CRISP-DM), which is the industry-independent de-facto standard for implementing data mining initiatives ( Schröer et al , 2021 ). This process has six phases, namely, business understanding, data understanding, data preparation, modelling, evaluation and deployment.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

et al. 2021

View full text Add to dashboard Cite

Background: The Malaysian government reacted to the pandemic’s economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled “Effects of COVID-19 on the Economy and Individual - Round 2,” collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques—Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes—were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance. Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

show abstract

“…Metodología CRISP-DM para el desarrollo de los modelos Fuente: (Schröer et al, 2021) Como se observa en la figura 1, la metodología CRISP-DM consta de seis etapas: Comprensión del problema o negocio, Comprensión de datos, Preparación de datos, Modelado, Evaluación del modelo e Implementación del modelo. La metodología CRISP-DM es una de las más empleadas actualmente para el desarrollo de proyectos de minería de datos.…”

Section: Figuraunclassified

“…La metodología CRISP-DM es una de las más empleadas actualmente para el desarrollo de proyectos de minería de datos. En 1997, se puso en marcha bajo el financiamiento del Programa de Investigación y Desarrollo en Tecnologías de Información de la Unión Europea (ESPRIT) (Huber et al, 2019;Schröer et al, 2021).…”

Section: Figuraunclassified

Análisis comparativo de técnicas de clasificación para determinar la deserción estudiantil de la facultad de ingeniería de la Universidad de Antioquia, Colombia

Chaparro¹,

Cuatindioy²,

Barrera³

2021

Espacios

View full text Add to dashboard Cite

El objetivo de este estudio es comparar diferentes algoritmos de clasificación de aprendizaje supervisado como las redes neuronales artificiales, métodos probabilísticos como regresión logística multinomial, métodos de ensamble como random forest, bagging, boosting y las máquinas de soporte vectorial, con el fin de identificar perfiles de posibles estudiantes desertores de la facultad de ingeniería de la Universidad de Antioquia, a partir de dos targets; número de créditos inscritos en último semestre y semestre en el cual el estudiante abandona la universidad.

show abstract

A Systematic Literature Review on Applying CRISP-DM Process Model

Cited by 306 publications

References 23 publications

Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games

Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games

COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

Análisis comparativo de técnicas de clasificación para determinar la deserción estudiantil de la facultad de ingeniería de la Universidad de Antioquia, Colombia

Contact Info

Product

Resources

About