Classification of Colorectal Cancer Using Clustering and Feature Selection Approaches

Nies, Hui Wen; Daud, Kauthar Mohd; Remli, Muhammad Akmal; Mohamad, Mohd Saberi; Deris, Safaai; Omatu, Sigeru; Kasim, Shahreen; Sulong, Ghazali

doi:10.1007/978-3-319-60816-7_8

Cited by 3 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[1,23] Euclidean distance Internal Compute distances between the objects to quantify their degree of dissimilarity. [19,31,34,109] Inter-cluster distance Internal Quantify the degree of separation between individual clusters. [11] Manhattan distance Internal Correspond to the sum of lengths of the other two sides of a triangle.…”

Section: Measurements Categories Usage Referencesmentioning

confidence: 99%

“…Measure the degree of confidence in a clustering assignment and lie in the interval [−1, +1], with well-clustered observations having values near +1 and near -1 for poorly clustered observations. [1,18,19,31,32,109] Square sum function of the error Internal Measure the quality of cluster either by compactness or homogeneity. [12,23,111] Entropy External Measure mutual information based on the probability distribution of random variables.…”

Section: Measurements Categories Usage Referencesmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

Self Cite

View full text Add to dashboard Cite

Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

show abstract

Section: Measurements Categories Usage Referencesmentioning

confidence: 99%

Section: Measurements Categories Usage Referencesmentioning

confidence: 99%

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, further study will limit the subject age from new-born to 16 years old for male and new-born to 15 years old for female, for age estimation, according to the supported literatures and the findings. The future study will improve the results of the age estimation by studying other algorithms used by various other case studies available such as by Lenin, Reddy, and Kalavathi [46], Ismail et al [45], Ismail et al [46], Khaleel et al [47] and all other classification methods [48][49][50][51][52][53][54][55][56][57].…”

Section: Resultsmentioning

confidence: 99%

Random forest age estimation model based on length of left hand bone for Asian population

Darmawan¹,

Abidin²,

Kasim³

et al. 2020

IJECE

Self Cite

View full text Add to dashboard Cite

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice.

show abstract

Feature Extraction and Classification of Colon Cancer Using a Hybrid Approach of Supervised and Unsupervised Learning

Ghosh

Sharma

Tomar

2021

Intelligent Systems Reference Library

View full text Add to dashboard Cite

Classification of Colorectal Cancer Using Clustering and Feature Selection Approaches

Cited by 3 publications

References 28 publications

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Random forest age estimation model based on length of left hand bone for Asian population

Feature Extraction and Classification of Colon Cancer Using a Hybrid Approach of Supervised and Unsupervised Learning

Contact Info

Product

Resources

About