Selection of Informative Genes to Classify Type 2 Diabetes Mellitus using Support Vector Machine

Auliah, Firda Nurul; Lawi, Armin; Thamrin, Sri Astuti; Budiman, Edy

doi:10.1109/icced46541.2019.9161111

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…One such process that has become a standard and popular, the 'Cross-Industry Standard Process for Data Mining' -or CRISP-DM -was proposed in the mid-1990s by a consortium of European companies to become a non-proprietary methodology standard for DM [6]. Figure 1, describes the data mining development life cycle of the process proposed in this study, which is a six sequential stage starting with a good understanding of the business and the need for a DM project and ending with a 'deployment' of a solution that satisfies specific business needs [7], [8]. .…”

Section: B Crisp-dm Analysis Modelmentioning

confidence: 99%

Fixed Voters Clustering to Determine the Level of Beginner Voters Using Data Mining Techniques

Hairah

2020

ETJ

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Data mining clustering technique is used to classify the level of beginner voters using the K-Means method. Fixed voter clusters are used for decision making for stakeholders regarding information on beginner voters in each district and sub-district. The error calculation method is used to measure the level of error value for each distance calculation used, the distance calculation method used ie Euclidean, Manhattan, and Minkowski Distance with the Means Square Error (MSE) approach to measure the level of the error value. The calculation results show that the lowest error occurs in the calculation of the Minkowski Distance model 3 cluster, where the error rate is 11%, while the highest error rate occurs in the calculation of the Manhattan Distance model 5 cluster, which is 38%.

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Section: B Crisp-dm Analysis Modelmentioning

confidence: 99%

Fixed Voters Clustering to Determine the Level of Beginner Voters Using Data Mining Techniques

Hairah

2020

ETJ

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Improved invasive weed bird swarm optimization algorithm (IWBSOA) enabled hybrid deep learning classifier for diabetic prediction

Aher

Jena

2022

J Ambient Intell Human Comput

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Discovery of type 2 diabetes mellitus with correlation and optimization driven hybrid deep learning approach

Middha,

Mittal

2023

Computer Methods in Biomechanics and Biomedical Engineering

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Selection of Informative Genes to Classify Type 2 Diabetes Mellitus using Support Vector Machine

Cited by 5 publications

References 14 publications

Fixed Voters Clustering to Determine the Level of Beginner Voters Using Data Mining Techniques

Fixed Voters Clustering to Determine the Level of Beginner Voters Using Data Mining Techniques

Improved invasive weed bird swarm optimization algorithm (IWBSOA) enabled hybrid deep learning classifier for diabetic prediction

Discovery of type 2 diabetes mellitus with correlation and optimization driven hybrid deep learning approach

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