AN OPTIMAL CLASSIFICATION MODEL FOR MICROARRAY CANCER DISEASE PREDICTION

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.

Download Full-text

Cytokine Profile Distinguishes Children With Plasmodium falciparum Malaria From Those With Bacterial Blood Stream Infections

The Journal of Infectious Diseases ◽

10.1093/infdis/jiz587 ◽

2019 ◽

Vol 221 (7) ◽

pp. 1098-1106

Author(s):

Nicole S Struck ◽

Marlow Zimmermann ◽

Ralf Krumkamp ◽

Eva Lorenz ◽

Thomas Jacobs ◽

...

Keyword(s):

Plasmodium Falciparum ◽

Bloodstream Infection ◽

Falciparum Malaria ◽

Prediction Accuracy ◽

Acute Infection ◽

Plasmodium Falciparum Malaria ◽

Classification Model ◽

Disease Prediction ◽

Asymptomatic Malaria ◽

Cytokine Profiles

Abstract Background Malaria presents with unspecific clinical symptoms that frequently overlap with other infectious diseases and is also a risk factor for coinfections, such as non-Typhi Salmonella. Malaria rapid diagnostic tests are sensitive but unable to distinguish between an acute infection requiring treatment and asymptomatic malaria with a concomitant infection. We set out to test whether cytokine profiles could predict disease status and allow the differentiation between malaria and a bacterial bloodstream infection. Methods We created a classification model based on cytokine concentration levels of pediatric inpatients with either Plasmodium falciparum malaria or a bacterial bloodstream infection using the Luminex platform. Candidate markers were preselected using classification and regression trees, and the predictive strength was calculated through random forest modeling. Results Analyses revealed that a combination of 7–15 cytokines exhibited a median disease prediction accuracy of 88% (95th percentile interval, 73%–100%). Haptoglobin, soluble Fas-Ligand, and complement component C2 were the strongest single markers with median prediction accuracies of 82% (with 95th percentile intervals of 71%–94%, 62%–94%, and 62%–94%, respectively). Conclusions Cytokine profiles possess good median disease prediction accuracy and offer new possibilities for the development of innovative point-of-care tests to guide treatment decisions in malaria-endemic regions.

Download Full-text

An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document

Wireless Communications and Mobile Computing ◽

10.1155/2020/8879054 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

G. Siva Shankar ◽

P. Ashokkumar ◽

R. Vinayakumar ◽

Uttam Ghosh ◽

Wathiq Mansoor ◽

...

Keyword(s):

Search Engine ◽

Side Information ◽

Classification Model ◽

Web Pages ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Web Document ◽

Exponential Increase ◽

Benchmark Datasets ◽

Optimal Classification

With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.

Download Full-text