NEURAL NETWORK OPTIMIZATION WITH PARTICLE SWARM OPTIMIZATION AND BAGGING METHODS ON CLASSIFICATION OF SINGLE PAP SMEAR IMAGE CELLS

In this study, an automatic diagnosis analysis of the results of pap smear image extraction using neural network algorithms, the analysis included a review of the results of Herlev pap smear extraction level 7 grade, 2 normal and abnormal classes, 3 classes of normal level dysplasia and 4 classes of abnormal dysplasia levels. The problem is that neural networks are very difficult to designate optimal features in diagnosing and difficult to handle class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize the features and bagging methods to deal with class imbalances, with the aim that the results of diagnosis using a neural network can increase its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the algorithm of network balance. At level 7 the buffer class increased by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 the class increased by 5.47%In this study, an automatic diagnosis analysis of the results of pap smear image extraction using neural network algorithms, the analysis included a review of the results of Herlev pap smear extraction level 7 grade, 2 normal and abnormal classes, 3 classes of normal level dysplasia and 4 classes of abnormal dysplasia levels. The problem is that neural networks are very difficult to designate optimal features in diagnosing and difficult to handle class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize the features and bagging methods to deal with class imbalances, with the aim that the results of diagnosis using a neural network can increase its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the algorithm of network balance. At level 7 the buffer class increased by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 the class increased by 5.47

A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach, the Linear Iterative Clustering process is employed to cluster the potential features of the preprocessed image, which is then combined with GrabCut to prevent the issues that arise when the number of superpixels is minimized. In addition, the proposed HLC-BC-GCST scheme benefits of the advantages of the Gaussian mixture model (GMM) on the extracted features from the iterative clustering method, based on which the mapping is performed to describe the energy function. Then, Bayes classification is used for reconstructing the graph cut model from the extracted energy function derived from the GMM model-based Linear Iterative Clustering features for better computation and implementation. Finally, the boundary optimization method is utilized to considerably minimize the roughness of cervical cells, which contains the cytoplasm and nuclei regions, using the GrabCut algorithm to facilitate improved segmentation accuracy. The results of the proposed HLC-BC-GCST scheme are 6% better than the results obtained by other standard detection approaches of cervical cancer using graph cuts

Improving Hierarchical Decision Approach for Single Image Classification of Pap Smear

The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification

Preventive Maintenance Decision Modeling in Health and Service Systems

This dissertation focuses on the preventive maintenance decision modeling in healthcare and service systems. In the first part of this dissertation, some issues in preventive health decisions for breast cancer are addressed, and in the second part, the required characteristics for preventive maintenance of an unreliable queuing system are derived. Adherence to cancer screening is the first issue that is addressed in this dissertation. Women’s adherence or compliance with mammography screening remained low in the recent years. In this dissertation, we first develop a design-based logistic regression model to quantify the probability of adherence to screening schedules based on women’s characteristics. In Chapter 3, we develop a randomized finite-horizon partially observable Markov chain to evaluate and compare different mammography screening strategies for women with different adherence behaviors in terms of quality adjusted life years (QALYs) and lifetime breast cancer mortality risk. The results imply that for the general population, the American Cancer Society (ACS) policy is an efficient frontier policy. In Chapter 4, the problem of overdiagnosis in cancer screening is addressed. Overdiagnosis is a side effect of screening and is defined as the diagnosis of a disease that will never cause symptoms or death during a patient\u27s lifetime. We develop a mathematical framework to quantify the lifetime overdiagnosis and mortality risk for different screening policies, and derive the (near) optimal policies with minimum overdiagnosis risk. In the second part, we consider an unreliable queuing system with servers stored in a shared stack. In such a system, servers have heterogeneous transient usage since servers on the top of the stack are more likely to be used. We develop a continuous-time Markov chain model to derive the utilization and usage time of servers in the system. These quantities are critical for the decision maker for deriving a maintenance policy

SURVEI PADA PENGGUNAAN TEKNIK DATA MINING PADA BIDANG KESEHATAN DI INDONESIA

Layanan kesehatan adalah salah satu sektor pelayanan masyarakat yang berkembang pesat saat ini, sehingga menghasilkan tumpukan data medical record pasien dalam jumlah besar. Tumpukan data tersebut dapat memberikan pengetahuan yang berharga jika diolah dengan cara yang tepat. Knowledge discovery in database (KDD) merupakan rangkaian proses untuk menggali pola pengetahuan yang tersembunyi dalam sekumpulan data yang besar. Teknik data mining adalah satu proses dalam KDD. Penelitian pada penggunaan teknik data mining dibidang kesehatan khususnya di Indonesia terus berkembang. Data mining dapat diterapkan untuk menemukan pola pengetahuan dari profil pasien dan data riwayat kesehatannya. Pengetahuan yang diperoleh dapat digunakan analisis dan pengambilan keputusan, antara lain untuk memprediksi jenis penyakit, mengetahui pola penyebaran penyakit, dan melihat efektifitas pengobatan. Makalah ini memaparkan berbagai penelitian penggunaan teknik data mining untuk analisis dan prediksi penyakit manusia. Teknik data mining yang dibahas antara lain asosiasi, klasifikasi, clustering, dan prediksi