Novel Stacking Classification and Prediction Algorithm Based Ambient Assisted Living for Elderly

Table 1

Related work summary.

Sr.no.	Author	Year	Findings
1	Liu et al. [20]	2020	The Pearson correlation coefficient technique was utilized for feature selection. It gives higher recognition ratios and reached an average of 1.56% and 2.7% F-measures
2	Helmi et al. [21]	2021	Hybrid GBO and GWO techniques were utilized for feature selection. As a result, it attained 98.13% precision
3	Nguyen et al. [22]	2018	Position-based feature selection algorithm was used. It attained 95.6% precision
4	Manzi et al. [23]	2017	Dynamic clustering technique used for skeleton data. It attained 93.3% accuracy
5	Cruciani et al. [24]	2020	A cluster-based semipopulation technique was proposed. It attained a 74.4% F-score
6	Fáñez et al. [25]	2019	K-means clustering, SVM, and KNN algorithms were used. It attained 87.50% accuracy
7	Li et al. [26]	2017	Multi-sensor data fusion technique is utilized. It attained 91.3% accuracy
8	Chelli et al. [27]	2019	KNN, ANN, QSVM and EBT techniques were utilized. It attained 81.2%, 87.8%, 93.2%, and 94.1%, respectively
9	Yacchirema et al. [28]	2019	Decision trees, ensemble, logistic regression, and Deepnets are used. It attained 94% accuracy