Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

2020 ◽  
Vol 4 (5) ◽  
pp. 805-812
Author(s):  
Riska Chairunisa ◽  
Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Random Forest ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Discrete Wavelet ◽  
Prostate Tumors ◽  
Cancer Data ◽  
Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.  

scholarly journals Anticancer Applications and Pharmacological Properties of Piperidine and Piperine: A Comprehensive Review on Molecular Mechanisms and Therapeutic Perspectives

2022 ◽  
Vol 12 ◽  
Author(s):  
Sicon Mitra ◽  
Uttpal Anand ◽  
Niraj Kumar Jha ◽  
Mahipal S. Shekhawat ◽  
Suchismita Chatterjee Saha ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Therapeutic Potential ◽  
Black Pepper ◽  
Piper Nigrum ◽  
Molecular Formula ◽  
Cycle Arrest

Piperine and piperidine are the two major alkaloids extracted from black pepper (Piper nigrum); piperidine is a heterocyclic moiety that has the molecular formula (CH2)5NH. Over the years, many therapeutic properties including anticancer potential of these two compounds have been observed. Piperine has therapeutic potential against cancers such as breast cancer, ovarian cancer, gastric cancer, gliomal cancer, lung cancer, oral squamous, chronic pancreatitis, prostate cancer, rectal cancer, cervical cancer, and leukemia. Whereas, piperidine acts as a potential clinical agent against cancers, such as breast cancer, prostate cancer, colon cancer, lung cancer, and ovarian cancer, when treated alone or in combination with some novel drugs. Several crucial signalling pathways essential for the establishment of cancers such as STAT-3, NF-κB, PI3k/Aκt, JNK/p38-MAPK, TGF-ß/SMAD, Smac/DIABLO, p-IκB etc., are regulated by these two phytochemicals. Both of these phytochemicals lead to inhibition of cell migration and help in cell cycle arrest to inhibit survivability of cancer cells. The current review highlights the pharmaceutical relevance of both piperine and piperidine against different types of cancers.

scholarly journals A DNA-Peptide Fusion as a Vaccine Candidate against Breast and Ovarian Cancer Metastasis: Consequent Benefit in Certain Lung Cancers

2020 ◽  
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Cancer Mortality ◽  
In Silico ◽  
Breast Cancer Mortality ◽  
Mortality Data ◽  
Published Data ◽  
Lung Carcinogenesis ◽  
Breast And Ovarian Cancer

Cancer mortality data were obtained from the WHO Mortality Database. Lung cancer, with about 85% being non-small cell lung cancer is one of the most common malignant tumors, considered the leading cause of cancer-related death in both men and women (associated with breast and ovarian cancer in metastasis). From published data, we designed a preventive vaccine in Silico aimed to protect against breast and ovarian cancer involved in metastasis for lung cancer. The largest increases are expected for melanoma; cancers of the prostate, kidney, liver, and urinary bladder in males; and the lung, breast, uterus, ovarian, and thyroid in females. Among all women, lung cancer mortality rates have surpassed those for breast cancer around the world. This reflects the decline of breast cancer mortality due to screening access and effective treatment alongside entrance of certain countries lifestyle and behavior in which smoking has become more prevalent in women. One aim of this research paper is to provide a better understanding for the potential dormant repositories of outbreaks and potential metastasis of breast and ovarian cancer and its consequents in lung cancer. In this study, we present to the cDNA-peptide fusion a more stable anti-tumoral against breast and ovarian cancer. As a cDNA target, we used primers from Her2 gene fusion with peptides from Her2 and human PARP-1 proteins. Our analysis identified 16 cloning DNA (cDNA) with theorical fusion stability (FS) value among 49.30-62.41 range and theorical Exosome Affinity (EA) (cDNA-peptide and exosome) among 62.60-77.10 range. We proposed a cDNA-peptide with theorical fusion value stability FS=50.36 Cruz and exosome affinity EA=68.02 Ro. We have named the cDNA-peptide selection as: LCR_2020_B008-55. In addition, in Silico, this cDNA-peptide also manifests partial inhibiting activity on the methylated promoter genes in lung tumors, therefore, this chimera cDNA-peptide may achieve a higher representative antitumoral activity against lung cancer disease. According to the anti-tumoral monitoring after and before vaccination using the candidate LCR_2020_B008-55, we proposed exosomes as biomarkers of lung carcinogenesis after and before vaccination. Due to the cDNA-peptides, in Silico, manifesting high affinity with exosomes, where our proposed vaccine may reach high representative activity against breast, ovarian and lung cancer in a metastasis stage, we identified this chimera with a triple antitumoral action.

THERAPIES OF RESISTANT EXUDATIVE PLEURITIS

2020 ◽  
pp. 22-30
Author(s):  
A.L. Charyshkin ◽  
E.A. Kuzmina ◽  
B.I. Khusnutdinov ◽  
E.A. Toneev ◽  
O.V. Midlenko ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Pleural Effusion ◽  
Malignant Pleural Effusion ◽  
Pleural Cavity ◽  
Pleural Empyema ◽  
Malignant Neoplasms ◽  
Risk Of Infection ◽  
Minimally Invasive Methods

In Russia, annually more than 100,000 people are diagnosed with tumor pleuritis. Resistant cancerous pleuritis is often caused by lung cancer (35 %), breast cancer (23 %), ovarian cancer and lymphomas (10 %). Pleuritis in malignant neoplasms often indicates the spread of the process through pleura. At the same time, systemic therapy does not always help patients. Radical treatment for malignant pleural effusion is gradually being replaced by new minimally invasive methods. Prolonged drainage of the pleural cavity in exudative pleuritis increases the risk of infection, which contributes to the development of pleural empyema. In order to eliminate the exudate, talc, tetracycline, and Betadine solutions are introduced into the pleural cavity through the drainage, the efficacy being 60 to 90 %. Thus, a new method for drug administration into the pleural cavity, which helps to eliminate resistant exudative pleuritis, remains relevant. Keywords: resistant exudative pleuriris, malignant neoplasms, pleurodesis. Проведен обзор отечественной и зарубежной литературы, посвященный местному лечению резистентного злокачественного плеврита. С каждым годом частота онкологических заболеваний и опухолевых плевритов только повышается. В России ежегодно опухолевые плевриты диагностируют более чем у 100 000 чел. Резистентный злокачественный плеврит в 35 % случаев обусловлен раком легкого, в 23 % – раком молочной железы, в 10 % – раком яичников и лимфомами. Плеврит при злокачественных новообразованиях часто свидетельствует о распространении процесса по плевре. При этом использование системных методов лечения не всегда облегчает состояние пациента. Радикальные методы лечения злокачественного плеврального выпота постепенно заменяются новыми минимально инвазивными методами. Продолжительное дренирование плевральной полости при экссудативном плеврите увеличивает риск ее инфицирования, что способствует развитию эмпиемы плевры. С целью ликвидации экссудата через установленный дренаж в плевральную полость вводят растворы талька, тетрациклина, бетадина с эффективностью от 60 до 90 %. Недостатком данного способа лечения является выраженный болевой синдром, повышение температуры тела, риск легочных осложнений, длительность лечения. Таким образом, создание способа введения лекарственных препаратов в плевральную полость для ликвидации резистентного экссудативного плеврита остается актуальным. Ключевые слова: резистентный экссудативный плеврит, злокачественные новообразования, плевродез.

Proximate Breast Cancer Factors Using Data Mining Classification Techniques

2019 ◽  
Vol 4 (1) ◽  
pp. 47-56
Author(s):  
Alice Constance Mensah ◽  
Isaac Ofori Asare
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Random Forest ◽  
Cancer Patients ◽  
Classification Model ◽  
Tree Model ◽  
Cancer Data ◽  
Data Mining Approach ◽  
Learning Techniques ◽  
Using Data

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors.

Oncology Questions and Answers

2013 ◽  
pp. 79-82
Author(s):  
Robert D. Ficalora
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Colon Cancer ◽  
Multiple Choice ◽  
Cancer Breast ◽  
Questions And Answers ◽  
Board Review ◽  
Cancer Côlon

Chapter 8 presents multiple-choice, board review questions on oncology including lung cancer, colon cancer, ovarian cancer, breast cancer, and prostate cancer. Full explanations are provided with the correct answers.

Lung Cancer Prediction Using Random Forest

2019 ◽  
Vol 13 ◽  
Author(s):  
A. Rajini ◽  
M.A. Jabbar
Keyword(s):  
Lung Cancer ◽  
Random Forest ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Early Stage ◽  
Random Forest Classifier ◽  
Cancer Dataset ◽  
Medical Practitioners ◽  
Cancer Data ◽  
Cancer Prediction

Background: In recent days, lung cancer is a familiar cancer across the globe. For the early prediction of lung cancer, medical practitioners and researchers require an efficient predictive model, which will reduce the number of deaths. In this paper, proposed a lung cancer prediction model by using random forest classifier, which aims at analyzing symptoms (gender, age, air pollution, weight loss, etc.). Objective: In this work, we address the problem of classification of lung cancer data using Random Forest Algorithm. Random Forest is the most accurate learning algorithm and many researchers in the health care domain use it. Method: This paper deals with the prediction of lung cancer by using Random Forest Classifier. Results: Proposed method (Random Forest Classifier) applied on two lung cancer datasets, achieved an accuracy of 100% for the lung cancer dataset-1 and 96.31 on dataset-2. In the prediction of lung cancer, the random forest has shown improved accuracy compared with other methods. Conclusion : This predictive model will help health professionals in predicting lung cancer at an early stage.

Breast Cancer Classification using Discrete Wavelet Transformation and Deep Learning

2019 ◽  
Vol 13 ◽  
Author(s):  
Emmanuel Masa-Ibi ◽  
Rajesh Prasad
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Cancer Classification ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network ◽  
Cancer Data ◽  
Breast Cancer Classification ◽  
The Neural Network

Background: One of the most prevalent sicknesses these days is breast cancer which is common amongst women. This sickness has been in increase to an alarming rate due to the lack of accurate administration of diagnoses. Early and accurate detection is one of the safest ways to cure a breast cancer patient. Objectives: The objective of this study is to proffer a more effective way to accurately classify a cancer sample; whether is Benign or Malignant. Methods: The classification model is based on the data collected from the UCI machine learning repository acquired from Wisconsin hospital called Wisconsin breast cancer data (WBCD). In this study, we preprocessed the dataset using DWT and then test the efficiency of deep learning (DL) for breast cancer classification. The model is developed using a feed-forward neural network and the result is compared with the observed values. Results: The result of the experiment proved the effectiveness of the proposed classification technique. The new technique accomplishes 98.90% accuracy for classifying breast cancer. Conclusions: The result from the experiment shows that the importance of data preprocessing and the efficiency of the neural network over other classification algorithms.

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