scholarly journals On the Feature Selection of Microarray Data for Cancer Detection based on Random Forest Classifier

2020 ◽  
Vol 12 (3) ◽  
Author(s):  
Tita Nurul Nuklianggraita ◽  
Adiwijaya Adiwijaya ◽  
Annisa Aditsania
Keyword(s):  
Random Forest ◽  
Cancer Detection ◽  
Microarray Data ◽  
Random Forest Classifier ◽  
World Health ◽  
Selection Operator ◽  
Health Organization ◽  
Microarray Technique ◽  
Selection Of ◽  
Better Than

Cancer is a disease that can affect all organs of humans. Based on data from the World Health Organization (WHO) fact sheet in 2018, cancer deaths have reached 9.6 million. One known way to detect cancer that is with Microarray Technique, but the microarray data have large dimensions due to the number of features that are very much compared to the number of samples. Therefore, dimension reduction should be made to produce optimum accuracy. In this paper, we compare Minimum Redundancy Maximum Relevance (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) to reduce dimension of microarray data. Moreover, by using Random Forest (RF) Classifier, the performance of classification (cancer detection) is compared. Based on simulation, it can be concluded that LASSO is better than MRMR because it can produce an evaluation of 100% in lung and ovarian cancer, 92% colon cancer, 93% prostate tumor and 83% central nervous system.

scholarly journals Discrete Wavelet Transform (DWT) and Random Forest for Cancer Detection Based on Microarray Data Classification

2020 ◽  
Vol 12 (3) ◽  
Author(s):  
Monica Triyani ◽  
Adiwijaya Adiwijaya ◽  
Annisa Aditsania
Keyword(s):  
Wavelet Transform ◽  
Random Forest ◽  
Discrete Wavelet Transform ◽  
Microarray Data ◽  
Large Data ◽  
World Health ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Health Organization ◽  
Better Than

Cancer is one of the leading causes of death worldwide. According to the World Health Organization (WHO) in 2018, about 9.6 million deaths caused by cancer. DNA microarray technology has played an important role in analyzing and diagnosing cancer. However, microarray data has a large data dimensions resulting in the accuracy of the Random Forest are not optimal. In this paper, the Discrete Wavelet Transform (DWT) is selected as a feature extraction method. Based on the simulation, the dimension can be reduced and improve the accuracy of classification up to 8% - 20%. DWT approximation coefficient can improve accuracy better than detailed coefficients for data on colon cancer 100%, lung cancer 100%, ovarian 100%, prostate tumor 85.71%, and central nervous system 83.33%.

scholarly journals IMPORTANCE OF CLASSICAL MORPHOLOGY IN THE DIAGNOSIS OF MYELODYSPLASTIC SYNDROME

2015 ◽  
Vol 7 ◽  
pp. e2015035 ◽  
Author(s):  
Rosangela Invernizzi ◽  
Federica Quaglia ◽  
Matteo Giovanni Della Porta
Keyword(s):  
Molecular Techniques ◽  
Classification Systems ◽  
World Health ◽  
Hematopoietic Stem ◽  
Morphological Alterations ◽  
Diagnosis And Classification ◽  
The Moment ◽  
Health Organization ◽  
Selection Of

Myelodysplastic syndromes (MDS) are hematopoietic stem cell disorders characterized by dysplastic, ineffective, clonal and neoplastic hematopoiesis. MDS represent a complex hematological problem: differences in disease presentation, progression and outcome  have necessitated the use of classification systems to improve diagnosis, prognostication and treatment selection. However, since a single biological or genetic reliable diagnostic marker has not yet been discovered for MDS, quantitative and qualitative dysplastic morphological alterations of bone marrow precursors and of peripheral blood cells are still fundamental for diagnostic classification. In this paper World Health Organization (WHO) classification refinements and current minimal diagnostic criteria proposed by expert panels are highlighted and related problematic issues are discussed. The recommendations should facilitate diagnostic and prognostic evaluations in MDS and selection of patients for new effective targeted therapies. Although in the future morphology should be supplemented with new molecular techniques, the morphological approach, at least for the moment, is still the cornerstone for the diagnosis and classification of these disorders.

Construction of the World Health Organization child growth standards: selection of methods for attained growth curves

2005 ◽  
Vol 25 (2) ◽  
pp. 247-265 ◽  
Author(s):  
E. Borghi ◽  
M. de Onis ◽  
C. Garza ◽  
J. Van den Broeck ◽  
E. A. Frongillo ◽  
...  
Keyword(s):  
World Health Organization ◽  
Growth Curves ◽  
Child Growth ◽  
World Health ◽  
Growth Standards ◽  
The World ◽  
Health Organization ◽  
Selection Of

scholarly journals Electronic Nose for Bladder Cancer Detection

2021 ◽  
Vol 5 (1) ◽  
pp. 22
Author(s):  
Heena Tyagi ◽  
Emma Daulton ◽  
Ayman S. Bannaga ◽  
Ramesh P. Arasaradnam ◽  
James A. Covington
Keyword(s):  
Bladder Cancer ◽  
Logistic Regression ◽  
Random Forest ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Electronic Nose ◽  
Random Forest Classifier ◽  
Urine Samples ◽  
Sparse Logistic Regression ◽  
High Separation

This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.

The Effect Of Fe Tablet Consumption On Hemoglobin (Hb) Increase In Pregnant Women: A Systematic Literature Review

2020 ◽  
Vol 6 (2) ◽  
pp. 8-13
Author(s):  
Admin midwiferia ◽  
Pratiwi Cahya Skania ◽  
Djaswadi Dasuki ◽  
Fitriana Siswi Utami
Keyword(s):  
Pregnant Women ◽  
Critical Appraisal ◽  
World Health ◽  
Exclusion Criteria ◽  
Iron Supplements ◽  
Level Increase ◽  
The World ◽  
Life Threatening ◽  
Health Organization ◽  
Selection Of

Anemia is still a problem in developing countries. The World Health Organization states that there are still more than 50 percent of women in the world suffering from anemia. anemia can cause life-threatening bleeding, miscarriage, low birth weight and premature birth. WHO defines anemia as a condition where the hemoglobin level is less than 11 mg / dL in the first and last trimester or 10.5 mg / dL in the second trimester or the hematocrit level is less than 37 percent. The study aim to determine the effect of Fe tablets consumption on hemoglobin (Hb) level increase in pregnant women and to find out the factors related to the compliance of pregnant women taking Fe tablets.  This Systematic Literature Publication and Science uses databases with the period 2008-2018. The selection of articles was based on the inclusion and exclusion criteria. The Appraisal study employed The Joanna Briggs Institute Critical Appraisal Tools. Taking Fe tablets is very influential in increasing levels of Hb in pregnant women who suffer from anemia. Effective iron supplements to reduce anemia in pregnancy. Support from family and closest people has an important role in increasing adherence to taking Fe tablets.

Web-Based Machine Learning Application for Heart Disease Prediction

2022 ◽  
pp. 383-393
Author(s):  
Lokesh M. Giripunje ◽  
Tejas Prashant Sonar ◽  
Rohit Shivaji Mali ◽  
Jayant C. Modhave ◽  
Mahesh B. Gaikwad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Web Application ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Web Based ◽  
The World ◽  
Comprehensive Survey ◽  
Health Organization

Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.

scholarly journals Adherence to WHO 2010 Recommendations in Relation to Semen Analysis Reports in the Laboratories of West Bengal

2019 ◽  
Vol 01 (04) ◽  
pp. 187-192
Author(s):  
Sujoy Dasgupta
Keyword(s):  
Observational Study ◽  
Reference Values ◽  
West Bengal ◽  
Semen Analysis ◽  
Sperm Concentration ◽  
Reproductive Technology ◽  
World Health ◽  
The World ◽  
Health Organization ◽  
Better Than

Background: In 2010, The World Health Organization (WHO) suggested the standards of reporting of semen analysis and the reference values. We tried to determine the adherence to the WHO 2010 standard regarding semen analysis among the laboratories of West Bengal. Methods: An observational study was carried out by collecting the semen analysis reports from different laboratories. Compliance with the WHO 2010 recommendations regarding the reporting of semen analysis and references mentioned was subsequently analyzed. Results: A total of 211 laboratory reports were collected; of which 15 were ART (Assisted Reproductive Technology)-laboratories (7%) and 196 were non-ART-laboratories (93%). More than half of the laboratories did not mention any reference values. Only 7.5% used the phrase “WHO 2010” as the reference. Only 3% of the laboratories reported all the six “important” parameters (volume, pH, sperm concentration, motility, morphology and vitality) and used the WHO 2010 references for all of them. The ART laboratories performed significantly better than their non-ART counterparts in reporting and quoting the WHO 2010 reference values. Conclusion: Even nine years after its introduction, the compliance with the WHO 2010 recommendations on semen analysis was still low among our laboratories. There is need for increased awareness for the laboratory persons in this regard.

scholarly journals Progress towards elimination of lymphatic filariasis in two districts of Maharashtra: scenario of last five years

2017 ◽  
Vol 4 (9) ◽  
pp. 3230 ◽  
Author(s):  
Sanjeev M. Chaudhary ◽  
Sanjay S. Kubde ◽  
Mohan B. Khamgaonkar
Keyword(s):  
Medical Officer ◽  
Cross Sectional Study ◽  
World Health ◽  
Community Based ◽  
Cross Sectional ◽  
Prevalence Data ◽  
Nagpur District ◽  
Sub Saharan ◽  
Health Organization ◽  
Selection Of

Background: Filaria was identified as one of the diseases to be eliminated globally and its global elimination by the year 2020 has been envisaged by World Health Organization (WHO). A large coverage- compliance gap has been found in many MDA programmes in India. Togo is the first sub-Saharan country to have stopped MDAs after prevalence data suggested that LF transmission had been interrupted. The successful Togo program demonstrates that LF elimination can be achieved in countries with limited resources. This study was undertaken to assess the situation of MDA coverage and compliance in two districts of Maharashtra.Methods: This is a community- based cross sectional study carried out in four selected clusters each in Nagpur and Bhandara districts of Maharashtra. Stratified random sampling is adopted for selection of households. In each district, 120 households are surveyed for the purpose of MDA evaluation every year. The coverage calculated in this article is programme coverage.Results: The coverage found in the year 2011 in Nagpur district was 63%, after which it was consistently rising every year. Similarly in Bhandara district, the coverage found was 70% in the year 2010, after which there was a rise every year. But the actual consumption rate was far less when compared to the coverage reported by the drug distributor, or the medical officer (more than 90% compliance is reported every year). Commonest reason for not consuming the drug was fear of side effects of the drug, which they must have experienced in the previous years activity, or seen other persons having side effects.Conclusions: Gradual increase in compliance of drug consumption over the period of five years in both the districts shows good progress towards the path of elimination. 

scholarly journals Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 551 ◽  
Author(s):  
Fayez AlFayez ◽  
Mohamed W. Abo El-Soud ◽  
Tarek Gaber
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
Survival Rates ◽  
Histogram Equalization ◽  
Breast Cancer Detection ◽  
Machine Learning Techniques ◽  
World Health ◽  
The World ◽  
Health Organization

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.

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