scholarly journals An Efficient Cancer Prediction System using Ensemble Methods

2020 ◽  
Vol 9 (4) ◽  
pp. 627-630
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Malignant Tumors ◽  
Ensemble Methods ◽  
Fatty Tissue ◽  
Tumor Type ◽  
Prediction System ◽  
The World ◽  
Cancer Tumor ◽  
Human Death

Breast cancer is the most dreadful disease in the world in past few decades. Many women in the world has been affected due to this horrible disease and died. Breast cancer occurs in breast cells, the fatty tissue or the fibrous connective tissue in the breast. Breast cancer is malignant tumors tend to become progressively worse leading to death. Factors such as age genetic mutations and a family’s reordered history in breast cancer can increase the risk of breast cancer. Two types of tumors: Benign: this tumor type is not dangerous for a human body and rarely causes human death. Malignant: this tumor type is more dangerous and causes human death, it is called breast cancer. Machine learning was the boon technique in the fields of the medical industry. By the development of machine learning and data analytics a decision making tool can be made which helps in early detection and diagnosis of cancer tumor in women. This concept is to study and develop a decision based tool to eradicate breast cancer. The prediction system makes use of the ensemble algorithms to detect the cancer at earlier stage. It also differentiates the type of cancer from which the patient is being affected with effective accuracy.

Development and clinical validation of Lantern Pharma’s AI engine: Response algorithm for drug positioning and rescue (RADR).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3114-3114
Author(s):  
Umesh Kathad ◽  
Yuvanesh Vedaraju ◽  
Aditya Kulkarni ◽  
Gregory Tobin ◽  
Panna Sharma
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Response Prediction ◽  
Supervised Machine Learning ◽  
Clinical Validation ◽  
Tumor Type ◽  
Patient Records ◽  
Number Of Patients

3114 Background: The Response Algorithm for Drug positioning and Rescue (RADR) technology is Lantern Pharma's proprietary Artificial Intelligence (Al)-based machine learning approach for biomarker identification and patient stratification. RADR is a combination of three automated modules working sequentially to generate drug- and tumor type-specific gene signatures predictive of response. Methods: RADR integrates genomics, drug sensitivity and systems biology inputs with supervised machine learning strategies and generates gene expression-based responder/ non-responder profiles for specific tumor indications with high accuracy, in addition to identification of new correlations of genetic biomarkers with drug activity. Pre-treatment patient gene expression profiles along with corresponding treatment outcomes were used as algorithm inputs. Model training was typically performed using an initial set of genes derived from cancer cell line data when available, and further applied to patient data for model tuning, cross-validation and final gene signature development. Model testing and performance computation were carried out on patient records held out as blinded datasets. Response prediction accuracy and sensitivity were among the model performance metrics calculated. Results: On average, RADR achieved a response prediction accuracy of 80% during clinical validation. We present retrospective analyses performed as part of RADR validation using more than 10 independent datasets of patients from selected cancer types treated with approved drugs including chemotherapy, targeted therapy and immunotherapy agents. For an instance, the application of the RADR program to a Paclitaxel trial in breast cancer patients could have potentially reduced the number of patients in the treatment arm from 92 unselected patients to 24 biomarker-selected patients to produce the same number of responders. Also, we cite published evidence correlating genes from RADR derived biomarkers with increased Paclitaxel sensitivity in breast cancer. Conclusions: The value of RADR platform architecture is derived from its validation through the analysis of about ~17 million oncology-specific clinical data points, and ~1000 patient records. By implementing unique biological, statistical and machine learning workflows, Lantern Pharma's RADR technology is capable of deriving robust biomarker panels for pre-selecting true responders for recruitment into clinical trials which may improve the success rate of oncology drug approvals.

Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features

2010 ◽  
pp. 27-53
Author(s):  
Geraldo Braz Júnior ◽  
Leonardo de Oliveira Martins ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Cause Of Deaths ◽  
Cancer Tumor ◽  
Detection And Diagnosis

Breast cancer is a malignant (cancer) tumor that starts from cells of the breast, being the major cause of deaths by cancer in the female population. There has been tremendous interest in the use of image processing and analysis techniques for computer aided detection (CAD)/ diagnostics (CADx) in digital mammograms. The goal has been to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation. CAD/CADx systems can aid radiologists by providing a second opinion and may be used in the first stage of examination in the near future, providing the reduction of the variability among radiologists in the interpretation of mammograms. This chapter provides an overview of techniques used in computer-aided detection and diagnosis of breast cancer. The authors focus on the application of texture and shape tissues signature used with machine learning techniques, like support vector machines (SVM) and growing neural gas (GNG).

scholarly journals Machine Learning Algorithms: Diagnosing Breast Cancer

2019 ◽  
Vol 8 (2S6) ◽  
pp. 849-851
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Benign Tumor ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Cancer Breast ◽  
Cancer Tumor ◽  
The Common

Breast Cancer has become one of the common diseases not only in women but also in few men. According to research, the demise rate of females has increased mainly because of Breast Cancer tumor. One out of every eight women and one out of every thousand men are diagnosed with breast cancer. Breast cancer tumors are mainly classified into two types: Benign tumor which is a non-cancerous tumor and other one is malignant tumor which is a cancerous tumor. In order to know which type of tumor a patient has; the accurate and early diagnosis is a very crucial step. Machine Learning (ML) algorithms have been used to develop and train the model for classification of the type of tumor. For accurate and better classification several classification algorithms in ML have been trained and tested on the dataset that was collected. Already algorithms like Naïve Bayes, Random Forest, K-Nearest Neighbor and SVM showed better accuracy for classification of tumor. When we implemented Multilayer Perceptron (MLP) algorithm it gave us the best accuracy levels among all both during training as well as testing .i.e. 97%. So, the exact classification using this model will help the doctors to diagnose the type of tumor in patients quickly and accurately

scholarly journals A review on breast cancer in Iraq and future therapies insights

2022 ◽  
Vol 3 (01) ◽  
pp. 3-15
Author(s):  
Nawfal Alrawi
Keyword(s):  
Breast Cancer ◽  
Anticancer Agents ◽  
Malignant Tumors ◽  
Breast Cancer Treatment ◽  
Cancer Type ◽  
Therapeutic Approaches ◽  
Medicinal Plant Extracts ◽  
The World ◽  
Iraqi Women ◽  
Periodic Examination

Cancer is one of the most common diseases around the world and the second leading cause of death after cardiovascular disease. Breast cancer is the most prevalent cancer type among Iraqi women, as it represents the highest percentage of malignant tumors in women until 2018. Therefore, women should be aware of the aggravation of this disease, the importance of the periodic examination for early detection for breast cancer, and following the most appropriate means for the treatment to get recovered and, thus, to reduce mortality. To fight cancer, there is an urgent need to search for new effective anticancer therapies that alter the molecular biology of tumor cells, stimulate the immune system, or specifically deliver chemotherapy factors directly to cancer cells without affecting normal cells and reducing the side effects of treatments. In this context, this paper aimed to highlight the therapeutic approaches used in the current researches of breast cancer treatment. Accumulated evidence showed that medicinal plant extracts, and can serve as anticancer agents. The proposed mechanisms were discussed and presented in this review.

Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features

2012 ◽  
pp. 769-792
Author(s):  
Geraldo Braz Júnior ◽  
Leonardo de Oliveira Martins ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Cause Of Deaths ◽  
Cancer Tumor ◽  
Detection And Diagnosis

Breast cancer is a malignant (cancer) tumor that starts from cells of the breast, being the major cause of deaths by cancer in the female population. There has been tremendous interest in the use of image processing and analysis techniques for computer aided detection (CAD)/ diagnostics (CADx) in digital mammograms. The goal has been to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation. CAD/CADx systems can aid radiologists by providing a second opinion and may be used in the first stage of examination in the near future, providing the reduction of the variability among radiologists in the interpretation of mammograms. This chapter provides an overview of techniques used in computer-aided detection and diagnosis of breast cancer. The authors focus on the application of texture and shape tissues signature used with machine learning techniques, like support vector machines (SVM) and growing neural gas (GNG).

EPIDEMIOLOGY AND SCREENING FOR BREAST CANCER

2017 ◽  
Vol 63 (3) ◽  
pp. 375-384
Author(s):  
Vladimir Semiglazov ◽  
Vakhtang Merabishvili ◽  
Vladislav Semiglazov ◽  
Aleksandr Komyakhov ◽  
Yevgeniy Demin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Malignant Tumors ◽  
Cancer Control ◽  
Scientific Publication ◽  
Healthy Population ◽  
Steady Growth ◽  
The World ◽  
Breast Cancer Epidemiology ◽  
Effective Program ◽  
Iarc Scientific Publication

To our estimations annually breast cancer is registered in more than 2 million women in the world (1018% of all malignant tumors). According to the latest edition of the IARC “Cancer in 5 continents” (V. X, IARC Scientific Publication №164) the maximum standardized rate is recorded over 100 0/0000 in Belgium, Italy and France. The minimum standardized rate (less than 40 0/0000) is marked in Cuba, Turkey and Ukraine. Paying attention to the steady growth of primary cases of breast cancer in the world that are in the first place in the structure of cancer, one of the main tasks of cancer control becomes mass periodic examinations of healthy population to detect latent cancer in such stage when it can be cured completely. The purpose of the study is to investigate breast cancer epidemiology at the present stage and to develop an effective program for breast cancer secondary prevention. Materials and methods. In order to perform the study for the preventive realization there were selected the most suitable methods of mass screening of practically healthy women, modified software accumulation, collection and analysis of data, conducted pilot development on the basis of out-patient departments and the Oncology Center of the Moscow District of St. Petersburg.

scholarly journals Early Breast Cancer Detection Utilizing Artificial Neural Network

2021 ◽  
Vol 18 ◽  
pp. 32-42
Author(s):  
Zakia Sultana ◽  
Md. Ashikur Rahman Khan ◽  
Nusrat Jahan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Malignant Tumors ◽  
Probabilistic Neural Network ◽  
The Body ◽  
Support Vector ◽  
Benign Tumors ◽  
Feed Forward Neural Network ◽  
Elman Neural Network ◽  
Cancer Tumor

Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs of the body. For that early detection of benign and malignant tumor is important for confining the death of breast cancer. In these research study different neural networks such as, Multilayer Perceptron (MLP) Neural Network, Jordan/Elman Neural Network, Modular Neural Network (MNN), Generalized Feed-Forward Neural Network (GFFNN), Self-Organizing Feature Map (SOFM) Neural Network, Support Vector Machine (SVM) Neural Network, Probabilistic Neural Network (PNN) and Recurrent Neural Network (RNN) are used for classifying breast cancer tumor. And compare the results of these networks to find the best neural network for detecting breast cancer. The networks are tested on Wisconsin breast cancer (WBC) database. Finally, the comparing result showed that Probabilistic Neural Network shows the best detection result than other networks.

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