Early Prediction of Ovarian Cancer from Images along with Some Blood Biomarkers Using Machine Learning
* Ovarian Cancer Data Collection
The related test data can help to tell if there is ovarian cancer or not and if ovarian cancer has spread to other organs.
* Data preprocessing:
Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis. When creating a machine learning project, it is not always a case that we come across the clean and formatted data. And while doing any operation with data, it is mandatory to clean it and put in a formatted way. So for this, we use data preprocessing task.
* Feature Extraction from Data
Feature extraction is a process of dimensionality reduction by which an initial set of raw data is reduced to more manageable groups for processing. A characteristic of these large data sets is a large number of variables that require a lot of computing resources to process. Feature extraction is the name for methods that select and /or combine variables into features, effectively reducing the amount of data that must be processed, while still accurately and completely describing the original data set.
* Feature Selection From Data
Feature selection is different from dimensionality reduction. Both methods seek to reduce the number of attributes in the dataset, but a dimensionality reduction method do so by creating new combinations of attributes, whereas feature selection methods include and exclude attributes present in the data without changing them.
* Learning Algorithm
SVM , Neural Network, KNN, Logistic Regression, Random Forest, etc.
Decision Tree, Linear Regression , Neural Network, SVR, Polynomial Regression etc.
* Model Training
Two model training styles are most common — supervised and unsupervised learning. The choice of each style depends on whether must forecast specific attributes or group data objects by similarities.
Supervised learning: Supervised learning allows for processing data with target attributes or labeled data.
Unsupervised learning: During this training style, an algorithm analyzes unlabeled data. The goal of model training is to find hidden interconnections between data objects and structure objects by similarities or differences.
* Model Performance Evaluation
For Classification: Accuracy, Sensitivity, Specificity
For Regression: R² ,MSE ,RMSE
Accuracy= (TN+TP)/(TN+TP+ FN+FP)
Sensitivity= TP/(TP+ FN)
Ovarian Cancer Prediction from Ovarian Cysts Based on TVUS Using Machine Learning Algorithms
Akter L., Akhter N.
Proceedings of the International Conference on Big Data, IoT, and Machine Learning. 2021 Dec 4; Volume 95: Pages 51-61
Ovarian Cancer Classification from Pathophysiological Complications using Machine Learning Techniques
L. Akter and N. Akhter
ICCCNT. 2021 Nov 13; Pages 1-6
Detection of Ovarian Malignancy from Combination of CA125 in Blood and TVUS Using Machine Learning
Akter L., Akhter N.
Proceedings of Intl. Conf. on Trends in Computational and Cognitive Engineering. 2020 Dec 17; Volume 1309: Pages 279-289