Post-Surgical Survival Forecasting of Breast Cancer Patient: A Novel Approach

Kaushik, Devender; Prasad, Bakshi Rohit; Sonbhadra, Sanjay Kumar; Agarwal, Sonali

doi:10.1109/icacci.2018.8554745

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Kaushik et al [11] have proposed a new approach for predicting the survival of breast cancer patients. The new approach, recommended for lumpectomy and mastectomy, utilizes Support Vector Machine Communication-Efficient Distributed Double Coordinate Ascent (SVM-CoCoA).…”

Section: Literature Reviewmentioning

confidence: 99%

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Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Bülbül,

Işık

2024

Biomimetics

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The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients’ clinical characteristics and findings to determine treatment plans, making accurate predictions essential for efficient resource utilization and optimal patient support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, and the Softmax Classifier was developed for predicting patient survival. The architecture was evaluated using the Haberman’s Survival dataset and the Echocardiogram dataset from UCI. The results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, and Gradient Bagging applied to the same datasets. The findings indicate that the proposed architecture outperforms other Machine Learning methods in predicting patient survival for both datasets and surpasses the results reported in the literature for the Haberman’s Survival dataset. In the light of the findings obtained, the models obtained with the proposed architecture can be used as a decision support system in determining patient care and applied methods.

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Section: Literature Reviewmentioning

confidence: 99%

“…The NDAF values depicted in Table 4 were randomly assigned based on the constraint function described in Equation ( 11): (11) where P i NDAF j (n) represents the NDAF value of particle i at iteration j. The activation functions corresponding to the randomly determined NDAF value are listed in Table 7.…”

Section: Neaf Valuesmentioning

confidence: 99%

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Bülbül,

Işık

2024

Biomimetics

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show abstract

“…In 2018, Kaushik [23] stated the survival forecasting that is postsurgical in case of cancer in the breast. ey have provided the novel concept related to the postsurgical survival forecasting of the breast cancer patient.…”

mentioning

confidence: 99%

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Madhu

Kumar

2021

Mathematical Problems in Engineering

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There are many research studies in the field of breast cancer prediction, but it has been observed that the time taken for prediction needs to be reduced. The problem in the existing research is space consumption by graphical content. The proposed research is supposed to minimize the prediction time and space consumption. In this paper, research has focused on the study of existing breast cancer research and techniques and eliminating their limitation. It has been observed that when the number of datasets increases, every comparison makes a huge gap in size and comparison time. This research proposes a methodology for breast cancer prediction using an edge-based CNN (convolutional neural network) algorithm. The elimination of useless content from the graphical image before applying CNN has reduced the time consumption along with space consumption. The edge detection mechanism would retail only edges from the image sample in order to detect the pattern to predict breast cancer. The proposed work is supposed to implement the proposed methodology. A comparison of the proposed methodology and algorithm with the existing algorithm is made during simulation. The proposed work is found to be more efficient compared to the existing techniques used in breast cancer prediction. The utilization of proposed in the work area of medical science is supposed to enhance the capability in case of CNN at the time of decision-making. The proposed work is supposed to be more accurate compared to the existing works. It has been observed that the proposed work is fourteen to fifteen percent more accurate. It is taking 9/4 times less space and 1.0849004/0.178971 times less time compared to the general CNN model. Accuracy might vary as per size of the image and alteration performed in dataset of the image.

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Impact of the Composition of Feature Extraction and Class Sampling in Medicare Fraud Detection

Kumari

Punn²,

Sonbhadra³

et al. 2023

Lecture Notes in Computer Science

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Post-Surgical Survival Forecasting of Breast Cancer Patient: A Novel Approach

Cited by 5 publications

References 3 publications

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Impact of the Composition of Feature Extraction and Class Sampling in Medicare Fraud Detection

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