An Improved CHI2 Feature Selection Based a Two-Stage Prediction of Comorbid Cancer Patient Survivability

Devi, Appari Geetha; Borra, Surya Prasada Rao; Haritha, Thotakura; Mandava, Venkata Subba Rao; Balaji, Tata; Sagar, Kalapala Vidya; Kodepogu, Koteswara Rao

doi:10.18280/ria.370111

Cited by 2 publications

(3 citation statements)

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“…Bayes' theorem is a way to resolve uncertain data by utilizing a formula called Bayes and an algorithm built on the premise that each pair of properties used to categorize something stands alone which is expressed as follows [21], [47]:…”

Section: E Bayes' Theorem Methodsmentioning

confidence: 99%

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Application of Forward Chaining Method, Certainty Factor, and Bayes Theorem for Cattle Disease

Putra,

Fadlil,

Umar

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Indonesia is a country that has many natural resources, especially mammals. The Papua and West Papua regions are large provinces with abundant natural resources and tremendous livestock potential. The availability of natural resources in the form of live cattle provides a great opportunity to develop animal husbandry in West Papua province. This research was conducted to create a new expert system with a knowledge base to solve the problems that occur and be useful for the community, especially cattle breeders. The current problem is the delay and lack of medical personnel in diagnosing cattle diseases, the distance that must be traveled, which is still very difficult to travel, and the lack of understanding of farmers in early handling when implications indicate animals. So, the Certainty Factor Method and Bayes Theorem with Forward-Chaining search are used to handle current problems. From the results of manual calculations, Certainty Factor Forward Chaining search is a method that has an uncertainty value of 99.84% for 3-day fever compared to Bayes Theorem Forward Chaining search with a value of 50% for worms, 50% for 3-day fever and 50% for nail rot, if applied then Certainty Factor Forward Chaining search is the most appropriate. Likewise, updating the knowledge base must be done from time to time. So that in the future, it can be compared with other methods and Android-based to facilitate current breeders.

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Section: E Bayes' Theorem Methodsmentioning

confidence: 99%

“…A study in [21] has shown comorbid conditions worsen the prognosis of cancer patients. With hazard ratios ranging from 1.1 to 5.8, most studies found that cancer patients with comorbidities had worse 5-year survival rates than those without.…”

Section: Introductionmentioning

confidence: 99%

Application of Forward Chaining Method, Certainty Factor, and Bayes Theorem for Cattle Disease

Putra,

Fadlil,

Umar

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The current research employed a neural network to identify the grade of cancer tissue. Images were categorized according to relevant classes, using the ProstrateX-2 challenge [12] as a benchmark. Existing models were examined for their ability to embed prostate zone segmentation as prior information and their use of ensemble techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Bottleneck Feature-Based U-Net for Automated Detection and Segmentation of Gastrointestinal Tract Tumors from CT Scans

Gandikota,

Abirami,

Kumar

2023

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In today's medical landscape, an array of diagnostic techniques for cancer, leveraging imaging data, have become increasingly prevalent. This has posed a unique challenge for radiologists in the detection of Digestive System Cancer (DSC). This paper introduces the Bottleneck Feature-based U-Net, an innovative method designed for the automated detection and segmentation of the digestive system utilizing endoscopy. The U-Net design, previously proven successful for image segmentation tasks, is harnessed to its full potential in our proposed method. We have enhanced its performance by integrating a bottleneck feature extraction technique. The encoding U-Net is initially trained prior to the training of the BS U-Net, facilitating the procurement of encodings from label maps containing crucial anatomical information, such as shape and location. A Bottleneck Supervised (BS) U-Net is thus formed by pairing an encoding U-Net with a segmentation U-Net. Our proposed bottleneck feature in the U-Network enables the model to compress input data, an essential learning component. This compressed view of data retains vital information used for either reconstructing the input image or carrying out the segmentation process. In the current study, we put forth a bottleneck-based U-Net model tailored to perform gastrointestinal tract tumor segmentation. To train and test our method, we employed the comprehensive Kvasir dataset, which encompasses a wide range of digestive system images. We further tested the robustness and generalizability of our model through a thorough quantitative and qualitative analysis. The results underscore the versatility of the bottleneck U-Net and its potential as a reliable tool for radiologists in clinical practice. Our proposed model demonstrated rapid and effective cancer diagnosis capabilities, thus reducing diagnosis time. The model exhibited an impressive accuracy rate of 98.64% and a specificity score of 99.71%, outperforming both LSTM-ANN and GA Algorithms. This not only attests to the efficacy of our model but also underscores its potential in advancing diagnostic methodologies in clinical settings.

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An Improved CHI2 Feature Selection Based a Two-Stage Prediction of Comorbid Cancer Patient Survivability

Cited by 2 publications

References 0 publications

Application of Forward Chaining Method, Certainty Factor, and Bayes Theorem for Cattle Disease

Application of Forward Chaining Method, Certainty Factor, and Bayes Theorem for Cattle Disease

Bottleneck Feature-Based U-Net for Automated Detection and Segmentation of Gastrointestinal Tract Tumors from CT Scans

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