scholarly journals Selección de un método de aprendizaje automático para clasificar patrones biomarcadores de lesiones precancerosas de las cuerdas vocales

2020 ◽  
pp. 1-7
Author(s):  
Xóchitl Siordia-Vásquez ◽  
Luz Yazmin Villagrán-Villegas ◽  
Miguel Patiño-Ortiz ◽  
Miguel Ángel Rojas-Hernández
Keyword(s):  
Decision Tree ◽  
Precancerous Lesions ◽  
Roc Curves ◽  
World Health ◽  
Vocal Cords ◽  
Biomarker Pattern ◽  
Cord Injury ◽  
Health Organization ◽  
Confusion Matrices ◽  
Iso 5725

The National Survey on Drug, Alcohol and Tobacco, 2016-2017, notes that 15.6 million Mexicans are active smokers and, by 2030, expect the death of 8 million cancers of the larynx or lung. Therefore, the World Health Organization (WHO) recommends detecting precancerous lesions of the larynx. This is possible, as they are characterized by a biomarker pattern manifested by the alteration of the biomechanical interpretation of the vocal cords, regardless of the sex and age of the smoker. The goal of this article is to evaluate three machine learning methods: neural networks, Gaussian networks, and decision tree to determine the method that best solves the problem of detecting patterns of precancerous vocal cord injury biomarkers. It uses the WEKA tool and a knowledge bank, endorsed by NOM-012-SSA3-2012, with 250 patterns, and provided by the Luis Guillermo Ibarra National Institute of Rehabilitation, Ibarra. The performance of the methods was compared by ROC curves and confusion matrices, under the criteria established by ISO-5725. The decision tree the method that best responds to the detection of patterns of biomarkers of precancerous lesions of the vocal cords.

Decision Tree-Based Analytics for Reducing Air Pollution

2018 ◽  
Vol 17 (02) ◽  
pp. 1850015 ◽  
Author(s):  
Ajanta Das ◽  
Anindita Desarkar
Keyword(s):  
Air Pollution ◽  
Decision Tree ◽  
World Health ◽  
Pollution Level ◽  
Rule Base ◽  
Premature Deaths ◽  
Id3 Algorithm ◽  
Rule Sets ◽  
Term Basis ◽  
Health Organization

Air pollution indicates contaminated air which arises due to the effect of physical, biological or chemical alteration to the air in the atmosphere applicable both for indoors and outdoors. This situation arises when poisonous gases, dust or smoke enter into the atmosphere and make the surroundings vulnerable for any living beings as well as difficult for them to survive. Large numbers of premature deaths happen across the globe if exposed to these pollutants on a long-term basis as major portion of the cities have the pollution level above the threshold determined by World Health Organization (WHO). So appropriate measures need to be taken on a priority basis to reduce air pollution as well as save our planet. This paper proposes a novel air pollution reduction approach which collects source pollution data. After extraction of source data, it uses various databases (DBs) and then different decisions or classes are created. The decision tree was created with the help of Iterative Dichotomiser 3 (ID3) algorithm to implement the rule base appropriately depending on the air pollution level and a bunch of rule sets were derived from the decision tree further.

scholarly journals Validation of the World Health Organization Disability Assessment Schedule 2.0 in adults with spinal cord injury in Taiwan: a psychometric study

Spinal Cord ◽  
2019 ◽  
Vol 57 (6) ◽  
pp. 516-524 ◽  
Author(s):  
Tzu-Ying Chiu ◽  
Monika E. Finger ◽  
Carolina S. Fellinghauer ◽  
Reuben Escorpizo ◽  
Wen-Chou Chi ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
World Health Organization ◽  
World Health ◽  
Disability Assessment ◽  
Psychometric Study ◽  
Cord Injury ◽  
The World ◽  
Health Organization

scholarly journals Quality of life of patients using intermittent urinary catheterization

2017 ◽  
Vol 25 (0) ◽  
Author(s):  
Laís Fumincelli ◽  
Alessandra Mazzo ◽  
José Carlos Amado Martins ◽  
Fernando Manuel Dias Henriques ◽  
Leonardo Orlandin
Keyword(s):  
Quality Of Life ◽  
World Health ◽  
Urinary Catheterization ◽  
Cross Sectional ◽  
World Health Organization Quality ◽  
Self Confidence ◽  
Psychological Domain ◽  
Cord Injury ◽  
Health Organization

ABSTRACT Objectives: measure and compare the quality of life of neurogenic bladder patients using intermittent urinary catheterization who were going through rehabilitation in Brazil and Portugal. Method: multicenter, quantitative, cross-sectional, observational-analytic and correlational study executed in Brazil and Portugal. Two data collection tools were used, being one questionnaire with sociodemographic and clinical data and the World Health Organization Quality of Life-bref. Patients were included who were over 18 years of age, suffering from neurogenic urinary bladder and using intermittent urinary catheterization. Results: in the sample of Brazilian (n = 170) and Portuguese (n = 52) patients, respectively, most patients were single (87-51.2%; 25-48.1%), had finished primary education (47-45.3%; 31-59.6%) and were retired (70-41.2%; 21-40.4%). Spinal cord injury was the main cause of using the urinary catheter in both countries. The Brazilian patients presented higher mean quality of life scores in the psychological domain (68.9) and lower scores in the physical domain (58.9). The Portuguese patients presented higher scores in the psychological domain (68.4) and lower scores in the environment domain (59.4). The execution of intermittent urinary self-catheterization was significant for both countries. Conclusions: in the two countries, these patients’ quality of life can be determined by the improvement in the urinary symptoms, independence, self-confidence, social relationships and access to work activities.

scholarly journals A validity study of the Spanish—World Health Organization Quality of Life short version instrument in persons with traumatic spinal cord injury

Spinal Cord ◽  
2018 ◽  
Vol 56 (10) ◽  
pp. 971-979 ◽  
Author(s):  
Sebastián Salvador-De La Barrera ◽  
Rubén Mora-Boga ◽  
Mª Elena Ferreiro-Velasco ◽  
Teresa Seoane-Pillado ◽  
Antonio Montoto-Marqués ◽  
...  
Keyword(s):  
Quality Of Life ◽  
World Health ◽  
Validity Study ◽  
Short Version ◽  
Traumatic Spinal Cord Injury ◽  
World Health Organization Quality ◽  
Cord Injury ◽  
Organization Quality ◽  
Health Organization

scholarly journals ANALISA 4 ALGORITMA DALAM KLASIFIKASI LIVER MENGGUNAKAN RAPIDMINER

2020 ◽  
Vol 6 (2) ◽  
pp. 1-9
Author(s):  
Annisa Putri Ayudhitama ◽  
Utomo Pujianto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
K Nearest Neighbor ◽  
Health Organization

Hati merupakan salah satu organ penting dalam tubuh manusia yang berfungsi untuk detoksifikasi racun atau penetral racun dari segala sesuatu yang masuk ke dalam tubuh kita, sehingga tubuh menjadi lebih sehat. Hati dapat terserang suatu penyakit yang mampu mengganggu tugasnya, apabila penyakit hati sudah menyerang maka racun akan tersebar ke seluruh tubuh dan membuat tubuh menjadi tidak sehat. Penyakit liver merupakan penyakit hati yang disebabkan oleh virus, alkohol, pola hidup dan lainnya. Menurut data WHO (World Health Organization) menunjukkan hampir 1,2 juta orang per tahun khususnya di Asia Tenggara dan Afrika mengalami kematian akibat terserang penyakit liver. Seseorang sering tidak menyadari atau terlambat mengetahui penyakit liver sehingga ketika diperiksa penyakit liver sudah parah, akan lebih baik apabila dilakukan penanganan lebih awal dengan mengetahui gejala-gejala yang diderita. Data mining mampu membantu diagnosa penyakit liver dengan lebih mudah terutama untuk membantu para dokter dalam menentukan apakah pasien menderita penyakit liver atau tidak, dengan gejala hampir mendekati penyakit liver. Proses diagnosa penyakit liver dilakukan dengan proses klasifikasi dan hasilnya berupa pasien tersebut menderita liver atau tidak. Penelitian ini menggunakan 4 algoritma data mining yaitu Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree dan Neural Network. Dataset yang digunakan yaitu Indian Liver Patient Dataset (ILPD) dari website UCI Machine Learning Repository. Keempat algoritma tersebut dibandingkan manakah yang lebih baik akurasinya untuk kasus diagnosa penyakit liver. Hasilnya menunjukkan bahwa algoritma Naïve Bayes memiliki akurasi 55,75%, algoritma K-Nearest Neigbor memiliki akurasi 66,36%, algoritma Decision Tree memiliki akurasi 67,04%, dan algoritma Neural Network memiliki akurasi 70,50%. Akurasi tersebut tergolong rendah karena kelas atau label antara pasien penyakit liver dan pasien tidak memiliki liver tidaklah seimbang, kelas pasien penyakit liver lebih banyak dibandingkan pasien tidak memiliki liver, sehingga banyak data yang diklasifikasikan sebagai pasien penyakit liver. Keywords— Data Mining, Decision Tree, Klasifikasi, KNN, Liver, Naïve Bayes, Neural Network

scholarly journals An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

2021 ◽  
Vol 17 (11) ◽  
pp. 176
Author(s):  
A Lakshmanarao ◽  
M Raja Babu ◽  
T Srinivasa Ravi Kiran
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Polynomial Regression ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Main Concern ◽  
Python Language ◽  
Precautionary Measures ◽  
Health Organization

<p>The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language</p>

scholarly journals Data Analysis and Computational Methods for Assessing Knowledge of Obesity Risk Factors among Saudi Citizens

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Alanazi Talal Abdulrahman ◽  
Dalia Kamal Alnagar
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Saudi Arabia ◽  
Decision Tree ◽  
Linear Models ◽  
World Health ◽  
Cross Sectional ◽  
Level Of Education ◽  
Awareness Campaigns ◽  
Health Organization

Introduction. According to the World Health Organization (2020), obesity is a growing problem worldwide. In fact, obesity is characterized as an epidemic. Objective. The aim of this paper is to use a logistic regression model as one of the generalized linear models and decision tree as one of the machine learning in order to assess the knowledge of the risk factors for obesity among citizens in Saudi Arabia. Methods and Materials. A cross-sectional questionnaire was given to the general population in KSA, using Google forms, to collect data. A total of 1369 people responded. Results. The findings showed that there is widespread knowledge of risk factors for obesity among citizens in Saudi Arabia. Participants’ knowledge of risk factors was very high (95.5%). In addition, a significant association was found between demographics (gender, age, and level of education) and knowledge of risk factors for obesity, in assessing variables for knowledge of the risk factors for obesity in relation to the demographics of gender and level of education. In addition, from decision tree results, we found that level of education and marital status were the most important variables to affect knowledge of risk factors for obesity among respondents. The accuracy of correctly classified cases was 95.5%, the same in logistic regression and decision tree. Conclusion. The majority of participants saw regular exercise and diet as an essential way to reduce obesity; however, awareness campaigns should be maintained in order to avoid complacency and combat the disease.

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