Stress Estimation Model for the Sustainable Health of Cancer Patients

Table 1

The summary of the related results published in recent years.

Publishing year	Objective	Approach	Results
2017 [17]	To study the relationships between mental health, parenting stress, and dyadic adjustment among first-time parents	Structural equation modeling	Showed the full intervention effect of mental health between dyadic adjustment and parenting stress. An analysis for multigroup observed that the paths did not vary across fathers and mothers.
2018 [18]	To examine the role of physical posttraumatic growth, posttraumatic growth, resilience, and mindfulness in the prediction of psychological and health-related adjustment	Confirmatory factor analysis and structural equation modeling	Forecasted quality of life and improvement of lower distress. The relationship between adjustment and resilience was noticed to be negotiated.
2019 [19]	To clear up the extent to which coping strategies, psychological symptoms, and social support interfere with good sleep quality and whether they arbitrate the relationship between fatigue and sleep quality or functional capacity of lung cancer patients.	Multivariate regression and mediation analyses	119 patients were enrolled, 58.2% of whom were found having a poor sleep because of cancer stress.
2020 [13]	To forecast heart disease which will help a physician in the diagnosis of heart disease at early stages	Rough sets and fuzzy rule-based classification with adaptive genetic algorithm	Main strengths of the presented model where it could efficiently tackle noisy data even on a huge number of attributes.
2021 [14]	To categorize the infant cries of a newborn into three groups such as hunger, discomfort, and sleep	Acoustic feature engineering and the variable selection using random forests	Showed a mean accuracy of around 91% for most situations, and this showed the capability of the suggested great gradient boosting-powered grouped-support-vector network in the classification of neonate cry. Also, the presented approach had a fast recognition rate of 27 seconds in the recognition of those emotional cries.
2021 [15]	To classify severe lymphoblastic leukemia from microscopic images of white blood cell	Image feature extractor and a classification head	Exhibited that using an XGBoost versus softmax classification head enhanced classification performance. Further, the attention map of the extracted features by Inception v3 for interpretation of the features learned by the presented model.
2022 [16]	To detect diabetic retinopathy at the early stages giving better results than other published approaches	Harris hawks optimization	The proposed model surpassed the other leading machine learning algorithms. However, training time was minimized. It was victimized to overfitting producing a negative impact on results when the original dataset was employed. The performance of the proposed approach had been improved even with an increased dataset size by two times.