• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.59-64
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• 2024

As the aging population grows, the incidence of cancer is increasing. Skin cancer appears externally, but people often don't notice it or simply overlook it. As a result, if the early detection period is missed, the survival rate in the case of late stage cancer is only 7.5-11%. However, the disadvantage of diagnosing, serious skin cancer is that it requires a lot of time and money, such as a detailed examination and cell tests, rather than simple visual diagnosis. To overcome these challenges, we propose an Attention-based CNN model skin cancer classification system. If skin cancer can be detected early, it can be treated quickly, and the proposed system can greatly help the work of a specialist. To mitigate the problem of image data imbalance according to skin cancer type, this skin cancer classification model applies the Over Sampling, technique to data with a high distribution ratio, and adds a pre-learning model without an Attention layer. This model is then compared to the model without the Attention layer. We also plan to solve the data imbalance problem by strengthening data augmentation techniques for specific classes.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6177-6180
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• 2014

Background: Our study objectives were to evaluate the medical economics of cervical cancer prevention and thereby contribute to cancer care policy decisions in Japan. Methods: Model creation: we created presence-absence models for prevention by designating human papillomavirus (HPV) vaccination for primary prevention of cervical cancer. Cost classification and cost estimates: we divided the costs of cancer care into seven categories (prevention, mass-screening, curative treatment, palliative care, indirect, non-medical, and psychosocial cost) and estimated costs for each model. Cost-benefit analyses: we performed cost-benefit analyses for Japan as a whole. Results: HPV vaccination was estimated to cost $291.5 million, cervical cancer screening $76.0 million and curative treatment $12.0 million. The loss due to death was $251.0 million and the net benefit was -$128.5 million (negative). Conclusion: Cervical cancer prevention was not found to be cost-effective in Japan. While few cost-benefit analyses have been reported in the field of cancer care, these would be essential for Japanese policy determination.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.5-10
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• 2016

Breast cancer, the second cause of cancer-related death after lung cancer and the most common cancer in women after skin cancer, is curable if detected in early stages of clinical presentation. Knowledge as to any age cut-off points which might have significance for prognostic groups is important in screening and treatment planning. Therefore, determining a change-point could improve resource allocation. This study aimed to determine if a change point for survival might exist in the age of breast cancer diagnosis. This study included 568 cases of breast cancer that were registered in Breast Cancer Research Center, Tehran, Iran, during the period 1986-2006 and were followed up to 2012. In the presence of curable cases of breast cancer, a change point in the age of breast cancer diagnosis was estimated using a mixture survival cure model. The data were analyzed using SPSS (versions 20) and R (version 2.15.0) software. The results revealed that a change point in the age of breast cancer diagnosis was at 50 years age. Based on our estimation, 35% of the patients diagnosed with breast cancer at age less than or equal to 50 years of age were cured while the figure was 57% for those diagnosed after 50 years of age. Those in the older age group had better survival compared to their younger counterparts during 12 years of follow up. Our results suggest that it is better to estimate change points in age for cancers which are curable in early stages using survival cure models, and that the cure rate would increase with timely screening for breast cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.2419-2423
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• 2012

Context: Genuine community participation does not denote taking part in an action planned by health care professionals in a medical or top-down approach. Further, community participation and health education on breast cancer prevention are not similar to other activities incorporated in primary health care services in Iran. Objective: To propose a model that provides a methodological tool to increase women's participation in the decision making process towards breast cancer prevention. To address this, an evaluation framework was developed that includes a typology of community participation approaches (models) in health, as well as five levels of participation in health programs proposed by Rifkin (1985&1991). Method: This model explains the community participation approaches in breast cancer prevention in Iran. In a 'medical approach', participation occurs in the form of women's adherence to mammography recommendations. As a 'health services approach', women get the benefits of a health project or participate in the available program activities related to breast cancer prevention. The model provides the five levels of participation in health programs along with the 'health services approach' and explains how to implement those levels for women's participation in available breast cancer prevention programs at the local level. Conclusion: It is hoped that a focus on the 'medical approach' (top-down) and the 'health services approach' (top-down) will bring sustainable changes in breast cancer prevention and will consequently produce the 'community development approach' (bottom-up). This could be achieved using a comprehensive approach to breast cancer prevention by combining the individual and community strategies in designing an intervention program for breast cancer prevention.

• Journal of Hospice and Palliative Care
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• v.24 no.4
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• pp.226-234
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• 2021

Purpose: The accessibility of medical facilities for cancer patients affects both their comfort and survival. Patients in rural areas have a higher socioeconomic burden and are more vulnerable to emergency situations than urban dwellers. This study examined the feasibility and effectiveness of a cancer care model integrating a regional cancer center (RCC) and public health center (PHC). Methods: This study analyzed the construction of a safety care network for cancer patients that integrated an RCC and PHC. Two public health institutions (an RCC in Gyeongnam and a PHC in Geochang County) collaborated on the development of the community care model. The study lasted 13 months beginning in February 2019 to February 2020. Results: The RCC developed the protocol for evaluating and measuring 27 cancer-related symptoms, conducted education for PHC nurses, and administered case counseling. The staff at the PHC registered, evaluated, and routinely monitored patients through home visits. A smartphone application and regular video conferences were incorporated to facilitate mutual communication. In total, 177 patients (mean age: 70.9 years; men: 59%) were enrolled from February 2019 to February 2020. Patients' greatest unmet need was the presence of a nearby cancer treatment hospital (83%). In total, 28 (33%) and 44 (52%) participants answered that the care model was very helpful or helpful, respectively. Conclusion: We confirmed that a combined RCC-PHC program for cancer patients in rural areas is feasible and can bring satisfaction to patients as a safety care network. This program could mitigate health inequalities caused by accessibility issues.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2893-2900
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• 2014

Background: Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients' survival times by using the Bayesian approach to predict future inferences of survival times. Materials and Methods: A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach is used to obtain the predictive survival times from the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtain the summary results for predictive inference. Results: The highest number of female breast cancer patients was found in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age at diagnosis (in years) was 60.92 (14.92). The mean (SD) survival time (in months) for female patients was 90.33 (83.10). The Exponentiated Exponential Model found better fits for the female survival times compared to the Exponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survival times. Conclusions: The findings with the proposed modeling strategy will assist healthcare researchers and providers to precisely predict future survival estimates as the recent growing challenges of analyzing healthcare data have created new demand for model-based survival estimates. The application of Bayesian will produce precise estimates of future survival times.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2895-2899
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• 2016

Background: Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005-2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. Materials and Methods: BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. Results: BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per $10^5$ women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019-20. Conclusions: BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.89-95
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• 2023

Analyzing breast cancer patient files is becoming an exciting area of medical information analysis, especially with the increasing number of patient files. In this paper, breast cancer data is collected from Khartoum state hospital, and the dataset is classified into recurrence and no recurrence. The data is imbalanced, meaning that one of the two classes have more sample than the other. Many pre-processing techniques are applied to classify this imbalanced data, resampling, attribute selection, and handling missing values, and then different classifiers models are built. In the first experiment, five classifiers (ANN, REP TREE, SVM, and J48) are used, and in the second experiment, meta-learning algorithms (Bagging, Boosting, and Random subspace). Finally, the ensemble model is used. The best result was obtained from the ensemble model (Boosting with J48) with the highest accuracy 95.2797% among all the algorithms, followed by Bagging with J48(90.559%) and random subspace with J48(84.2657%). The breast cancer imbalanced dataset was classified into recurrence, and no recurrence with different classified algorithms and the best result was obtained from the ensemble model.

• Korean Journal of Adult Nursing
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• v.18 no.1
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• pp.115-124
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• 2006

Purpose: The purpose of this study was to propose a theoretical model of hope commonly held by the cancer patients just after surgery, under the assumptions that hope of those patients is not only realistic and disease oriented but in dialectical circulation. Method: A theoretical model was generated through 4 steps: exploring a hope structure by synthesizing the relevant hope structures expressed in Kim and Tae's studies, in-depth literature review, examining the meanings of the concepts consisted of the structure in use and their causal relations in logical adequacy, proposing a theoretical structure through synthesizing the causal relations, and diagramming the structure. Results: The proposed theoretical model involves concepts such as Cancer Related Uncertainty (CRU), Efforts to Find out the Possibility of Cure or Recovery (EFPCR), and Hopefulness or Hopelessness. The 'EFPCR' is stipulated as 'Behaviors Related to Looking for Evidences or Cues (BRLEC)' and 'Formation of Cognitive Schema (FCS)'. In the model, Hopefulness is directly influenced by 'CRU in low', which is affected by 'FCS in good' from the result of EFPCR started with 'CRU in increase' while 'CRU with increase' from the result from EFPCR has direct effect on Hopelessness. Conclusion: The theoretical model would be used to enhancing hope of the cancer patients in post-operation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.655-657
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• 2021

It is challenging to find breast ultrasound image training dataset to develop an accurate machine learning model due to various regulations, personal information issues, and expensiveness of acquiring the images. However, studies targeting transfer learning for ultrasound breast cancer images classification have not been able to achieve high performance compared to radiologists. Here, we propose an improved transfer learning model for ultrasound breast cancer classification using publicly available dataset. We argue that with a proper combination of ImageNet pre-trained model and optimizer, a better performing model for ultrasound breast cancer image classification can be achieved. The proposed model provided a preliminary test accuracy of 99.5%. With more experiments involving various hyperparameters, the model is expected to achieve higher performance when subjected to new instances.