Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective

Asaduzzaman, Sayed; Ahmed, Md. Raihan; Rehana, Hasin; Chakraborty, Setu; Islam, Md. Shariful; Bhuiyan, Touhid

doi:10.1186/s12859-021-04131-6

Cited by 21 publications

(10 citation statements)

References 24 publications

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“…Multivariate adjustment and multiple-regression techniques were introduced for prediction (that is, for estimating the predicted value of a certain outcome as a function of given values of independent variables) [ 82 ]. AI studies using machine learning principles have focused on algorithms to predict cervical cancer [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. The most important predictors of cervical cancer were age, age at first sexual intercourse, number of sexual partners, pregnancies, smoking, period of smoking (years), hormonal contraceptives, period of use of hormonal contraceptives (years), IUD, period of use of IUD (years), STDs, period of STDs (years), Schiller, Hinselmann, cytology, the presence of 15 high-risk HPV genotypes [ 55 , 56 , 57 , 58 , 60 , 84 ], social status, marital status, personal health level, education level, and the number of caesarean deliveries [ 63 ].…”

Section: Resultsmentioning

confidence: 99%

“…However, according to Mudawi et al, certain characteristics of the patient samples, including the quantity of alcohol consumed and the presence of HIV and HSV2, could not be regarded as reliable predictors [ 95 ]. Based on the data shown in Table 1 , the accuracy of the algorithms in predicting cervical cancer varied from 70% to 100% [ 55 , 56 , 57 , 58 , 59 , 60 , 62 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. The application of AI for the prediction of cervical cancer is shown in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

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Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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“…Siddiqui et al (2021) developed a wearable sensor-based platform to identify autism spectrum disorder (ASD) using machine learning. Asaduzzaman et al (2021) developed a system by using machine learning and data mining approach to predict the risk level of cervical and ovarian cancer in association to stress. Existing research has proved that the machine learning model has achieved a performance that is not inferior to the traditional scale measurement.…”

Section: Recognition Methods Of Decision-making Style and Psychologic...mentioning

confidence: 99%

Automatic Decision-Making Style Recognition Method Using Kinect Technology

et al. 2022

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In recent years, somatosensory interaction technology, represented by Microsoft’s Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.

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“…AUROC is a performance metric for discrimination that evaluates the model's ability to distinguish between cases and non-cases. [28][29] [30].…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Prediction of Heart Diseases by Using Supervised Machine Learning Algorithms

Muhammed¹,

Abdul-Majeed²,

Mahmoud³

2023

wjps

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Heart Disease is a complex and life-threatening ailment that poses a significant mortality risk around the world, with nearly a third of global deaths attributable to heart-related conditions. The early prediction and detection of heart disease are of utmost importance in the medical field, as they may lead to saving numerous lives. However, the lack of heart expertise in many countries and the high rates of misdiagnosis highlight the need for accurate and efficient prediction methods. Machine learning-based approaches have the potential to address this need, particularly in handling the large amounts of data generated by medical sectors and hospitals. In this study, the performance and accuracy of several supervised machine learning algorithms were compared for heart disease prediction using a dataset obtained from PhysioNet databases. The classifiers that were applied included Artificial Neural Network (ANN), Gradient Boosting, Decision Tree, Naive Bayes, and Random Forest. Results showed that the ANN algorithm achieved the highest Accuracy of 94.1%, with a sensitivity and specificity of 94.1%. The study thus concluded that supervised machine learning techniques can be utilized with great success to forecast heart disease, displaying exceptional potential for practical application and accuracy

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Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective

Cited by 21 publications

References 24 publications

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Automatic Decision-Making Style Recognition Method Using Kinect Technology

Prediction of Heart Diseases by Using Supervised Machine Learning Algorithms

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