Multiclass Classification of Risk Factors for Cervical Cancer Using Artificial Neural Networks

World Health Organization statistics show that cervical cancer is the fourth most frequent cancer in women with an estimated 530,000 new cases in 2012. Cervical cancer diagnosis typically involves liquid-based cytology (LBC) followed by a pathologist review. The accuracy of decision is therefore highly influenced by the expert’s skills and experience, resulting in relatively high false positive and/or false negative rates. Moreover, given the fact that the data being analyzed is highly dimensional, same reviewer’s decision is inherently affected by inconsistencies in interpreting the data. In this study, we use an Artificial Neural Network based model that aims to considerably reduce experts’ inconsistencies in predicting cervical cancer. We rely on standard machine learning techniques to train the neural network using six experts’ predictions for cervical cancer (based on analysis of more than sixty parameters/risk factors) and we produce a model where the unanimous decision is predicted with very good accuracy

Prediksi Diagnosa Kanker Serviks Berdasarkan Informasi Demografi, Kebiasaan, dan Rekam Medis Menggunakan Algoritma Support Vector Machine

Penyakit kanker serviks merupakan salah satu penyakit yang dianggap sebagai penyakit paling mematikan di seluruh dunia. Menurut International Agency for Research on Cancer 2012, kanker serviks menempati peringkat ketiga pada penyakit yang sering diderita oleh wanita di seluruh dunia, dan menempati peringkat kedua di Indonesia. Menurut para ahli kanker, kanker serviks adalah salah satu jenis kanker yang paling dapat dicegah dan paling dapat disembuhkan dari semua kasus kanker. Salah satu kegiatan deteksi dini kanker serviks yang paling umum di Indonesia adalah menggunakan metode pap smear. Namun tersedianya data histori rekam medis pasien tidak disertai dengan proses ekstraksi data menjadi sebuah informasi yang dapat berguna untuk keputusan klinis. Konsep klasifikasi merupakan bagian dari teknik data mining yang memiliki pekerjaan utama melakukan analis prediksi. Telah terdapat banyak penelitian untuk melakukan prediksi dan klasifikasi pada kasus medis, dan menghasilkan klasifikasi dengan tingkat akurasi yang cukup tinggi, dengan nilai akurasi > 80 %. Sehingga diperlukan lebih banyak penelitian untuk mendapatkan suatu model yang mampu mengklasifikasikan dengan tingkat kesalahan minimal dan model yang dihasilkan dapat digunakan untuk melakukan prediksi data berikutnya. Penelitian ini menggunakan metode Cross Validation agar proses training lebih akurat dan menghasilkan prediksi yang lebih baik. Percobaan prediksi dilakukan dengan SVM kernel linear dan RBF. Atribut yang digunakan berjumlah 27 atribut, dengan 1 atribut target yaitu hasil tes biopsy pasien. Data yang digunakan berjumlah 668 data dan 200 data yang telah dilakukan resample data. Hasil dari penelitian ini merupakan hasil prediksi yang dilakukan pada dataset dari pasien ‘Hospital Universitario de Caracas’ di Caracas, Venezuela, dengan menggunakan algoritma Support Vector Machine. Hasil prediksi terbaik didapatkan dengan 200 data yang telah dilakukan resample dan menggunakan SVM kernel RBF parameter C > 1, dan γ > 10. Percobaan tersebut menghasilkan nilai akurasi sebesar 92 %, precision 75 %, recall 71 %, dan f-measure 72.77 %. ======================================================================================================== Cervical cancer is one of the most chronic disease in the world. According to the Internationl Agency for Research on Cancer 2012, cervical cancer ranks third in disease that is often afflicted by women in the world, and ranks second in Indonesia. According to cancer experts, cervical cancer is one of the most preventable and most curable cancers of all cancer cases. One of the most common cervical cancer detection activities in Indonesia is the Pap Smear method. However, the availability of patient medical record history data is not accompanied by data extraction process to an information that can be useful for clinical decisions. The concept of classification is part of the data mining technique that have the main work of doing predictive analysis. There have been many studies to make predictions and classifications in medical cases and produce a classification result with high accuracy rate, with an accuracy > 80 %. So it needs more research to get a model that is able to classify with minimal error rate and the model can be used to predict in the next data. This research use Cross Validation method to make the training process more accurate and get better prediction result. The prediction experiments were done with SVM linear and RBF kernel. Use 27 attributes with 1 target attribute that is the result of patient’s biopsy test. Total data that used on this research is 668 data and 200 resampled data. The result of this research are the result of prediction performed on the dataset of patient ‘Hospital Universitario de Caracas’ in Caracas, Venezuela, using Support VectorMachine algorithm. The best prediction results were obtained with 200 resampled data and using SVM RBF kernel with parameters C > 1 and γ > 10. The experiments resulted in an accuracy of 92 %, precision 75 %, recall 71 % and f-measure 72.77%

Promoting breast cancer screening among Chinese American women through young children\u27s theatrical performance

Research has revealed that underutilization of breast cancer screening by ethnic minorities often is related to language difficulties and cultural values and beliefs about cancer. The problem addressed in this secondary data analysis was the late diagnosis of breast cancer in the Chinese immigrant community. The purpose of the quasi-experimental study was to test the efficacy of a theatrical preschool performance, guided by the diffusion of innovation theory, in educating Chinese American women about breast cancer detection. The research questions sought to determine whether the performance increased the participants\u27 knowledge of breast cancer screening guidelines and whether country of origin, length of stay in the United States, and self-reported attentiveness were associated with knowledge gain of breast cancer screening guidelines. The preschool performance was performed by Chinese children ages 3 to 5 who displayed breast health guidelines from the Susan G. Komen for the Cure. One hundred and seventy-seven pre- and postperformance surveys were collected from a sample of Chinese women (84% foreign born). The secondary data were analyzed using standard linear regression analyses and bivariate logistic regressions. The findings demonstrated that promoting breast health screening guidelines among Chinese American women through a preschool theatrical performance significantly increased the participants\u27 knowledge of the guidelines. However, no major impact was detected between knowledge score and attentiveness to the theatrical performance and any of the demographic variables. Health care professionals can foster social change by adapting a preschool theatrical performance to educate ethnic communities on cancer control guidelines for early detection

Improved Weighted Random Forest for Classification Problems

Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models. The key to make well-performing ensemble model is in the diversity of the base models. Of the most common solutions for introducing diversity into the decision trees are bagging and random forest. Bagging enhances the diversity by sampling with replacement and generating many training data sets, while random forest adds selecting a random number of features as well. This has made the random forest a winning candidate for many machine learning applications. However, assuming equal weights for all base decision trees does not seem reasonable as the randomization of sampling and input feature selection may lead to different levels of decision-making abilities across base decision trees. Therefore, we propose several algorithms that intend to modify the weighting strategy of regular random forest and consequently make better predictions. The designed weighting frameworks include optimal weighted random forest based on ac-curacy, optimal weighted random forest based on the area under the curve (AUC), performance-based weighted random forest, and several stacking-based weighted random forest models. The numerical results show that the proposed models are able to introduce significant improvements compared to regular random forest

Selective Neuron Re-Computation (SNRC) for Error-Tolerant Neural Networks

Artificial Neural networks (ANNs) are widely used to solve classification problems for many machine learning applications. When errors occur in the computational units of an ANN implementation due to for example radiation effects, the result of an arithmetic operation can be changed, and therefore, the predicted classification class may be erroneously affected. This is not acceptable when ANNs are used in many safety-critical applications, because the incorrect classification may result in a system failure. Existing error-tolerant techniques usually rely on physically replicating parts of the ANN implementation or incurring in a significant computation overhead. Therefore, efficient protection schemes are needed for ANNs that are run on a processor and used in resource-limited platforms. A technique referred to as Selective Neuron Re-Computation (SNRC), is proposed in this paper. As per the ANN structure and algorithmic properties, SNRC can identify the cases in which the errors have no impact on the outcome; therefore, errors only need to be handled by re-computation when the classification result is detected as unreliable. Compared with existing temporal redundancy-based protection schemes, SNRC saves more than 60 percent of the re-computation (more than 90 percent in many cases) overhead to achieve complete error protection as assessed over a wide range of datasets. Different activation functions are also evaluated.This research was supported by the National Science Foundation Grants CCF-1953961 and 1812467, by the ACHILLES project PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Science and Innovation and by the Madrid Community research project TAPIR-CM P2018/TCS-4496.Publicad

Deep Synthesis of Realistic Medical Images: A Novel Tool in Clinical Research and Training

Making clinical decisions based on medical images is fundamentally an exercise in statistical decision-making. This is because in this case, the decision-maker must distinguish between image features that are clinically diagnostic (i.e., signal) from a large amount of non-diagnostic features. (i.e., noise). To perform this task, the decision-maker must have learned the underlying statistical distributions of the signal and noise to begin with. The same is true for machine learning algorithms that perform a given diagnostic task. In order to train and test human experts or expert machine systems in any diagnostic or analytical task, it is advisable to use large sets of images, so as to capture the underlying statistical distributions adequately. Large numbers of images are also useful in clinical and scientific research about the underlying diagnostic process, which remains poorly understood. Unfortunately, it is often difficult to obtain medical images of given specific descriptions in sufficiently large numbers. This represents a significant barrier to progress in the arenas of clinical care, education, and research. Here we describe a novel methodology that helps overcome this barrier. This method leverages the burgeoning technologies of deep learning (DL) and deep synthesis (DS) to synthesize medical images de novo. We provide a proof-of-principle of this approach using mammograms as an illustrative case. During the initial, prerequisite DL phase of the study, we trained a publicly available deep learning neural network (DNN), using open-sourced, radiologically vetted mammograms as labeled examples. During the subsequent DS phase of the study, the fully trained DNN was made to synthesize, de novo, images that capture the image statistics of a given input image. The resulting images indicated that our DNN was able to faithfully capture the image statistics of visually diverse sets of mammograms. We also briefly outline rigorous psychophysical testing methods to measure the extent to which synthesized mammography were sufficiently alike their original counterparts to human experts. These tests reveal that mammography experts fail to distinguish synthesized mammograms from their original counterparts at a statistically significant level, suggesting that the synthesized images were sufficiently realistic. Taken together, these results demonstrate that deep synthesis has the potential to be impactful in all fields in which medical images play a key role, most notably in radiology and pathology