Early risk prediction of cervical cancer: A machine learning approach

Ratul, Ishrak Jahan; Al-Monsur, Abdullah; Tabassum, Bushra; Ar-Rafi, Abrar Mohammad; Nishat, Mirza Muntasir; Faisal, Fahim

doi:10.1109/ecti-con54298.2022.9795429

Cited by 32 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, most studies did not utilize ML and data mining techniques. These techniques are being applied in numerous research studies to construct automated healthcare assistance systems that help experts forecast and prescribe solutions early [ [21] , [22] , [23] , [24] , [25] ]. Some earlier studies presented stress prediction techniques based on machine learning and data mining techniques for various target users [ [26] , [27] , [28] ].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach

Ratul

Nishat

Faisal

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach

Ratul

Nishat

Faisal

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…However, clinical assessment of mass undergraduate students would be unwieldy and resource-heavy, since there can be numerous factors involved in instigating depression. In this regard, Machine Learning (ML) models [16][17][18][19][20][21][22][23][24][25] can perhaps become valuable for detecting and predicting subsequent health issues [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] as well as depressive episodes. Furthermore, the result can be analyzed to identify depression-related trends revealed among young people which can aid higher education institutions to understand the factors better and develop effective strategies to mitigate these factors.…”

Section: Introductionmentioning

confidence: 99%

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

Siraji,

Rahman,

Nishat

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

show abstract

“…An ample amount of literature exists on the successful executions of the Bio-Inspired Algorithms in the field of power converters professed by many researchers [11][12][13][14][15][16][17][18][19][20]. One of such promising instances of work has been witnessed with Machine learning (ML) algorithms [21][22][23][24][25][26][27][28][29], artificial intelligence [30][31][32][33][34][35][36][37][38][39], and different applications of ML in healthcare sectors [40][41][42][43][44][45][46][47][48][49], and so many other cases [50][51][52][53][54][55][56][57][58][...…”

Section: Introductionmentioning

confidence: 99%

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

Shagor,

Faisal,

Nishat

et al. 2023

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

This paper represents the idea of implementing bio-inspired algorithms in designing an optimized PID controller to investigate the stability and improve the performance of the closed-loop CUK converter. Bio-inspired algorithms (BIA) such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and, Artificial Bee Colony (ABC) are stochastic optimization techniques and have been increasingly applied to attain optimal solution for designing and optimizing power converters in current times. The closed-loop transfer function of the CUK converter has been developed by the State Space Average (SSA) technique. This paper assesses both the cases of converter stability when it is integrated with the conventional PID controller and the BIA-PID controllers (FA-PID, PSO-PID, ABC-PID) and eventually compares the outcomes from all the controllers. For examining the stability of the system, three objective functions (IAE, ITAE, and ISE) and various performance specifications such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated. MATLAB and Simulink are used to carry out the simulations meticulously. Hence, a comparative analysis is illustrated in this paper to state a clear-sighted evaluation of the performances.

show abstract

Early risk prediction of cervical cancer: A machine learning approach

Cited by 32 publications

References 29 publications

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

Contact Info

Product

Resources

About