(untitled) (Journal of Pharmaceutical Negative Results, 2022)
Human health diseases are caused due to genetic disorders, environmental conditions, presence of Alpha synuclein bodies, etc.Identification of this disease can be done via estimation of body tremors, slowed movements, fever, change in corpuscles, rigid muscles,impaired posture & balance, pain analysis, loss of automatic movements, speech changes, and writing changes. To estimate theseeffects, researchers have proposed a wide variety of machine learning techniques that can analyze speech signals, electroencephalogram(EEG) signals, electrocardiogram (ECG) signals, handwriting variations, gait identification & classification, etc. These models arehighly variant in terms of their internal operating characteristics, which makes it ambiguous for clinical designers to identify optimalmodels for their deployments. Furthermore, these models also showcase highly variant performance levels as per their input featuresets. Thus, it is difficult to identify optimal models for a given set of input parameters. To overcome these issues, this paper initiallyreviews a wide variety of Human Health disease identification models in terms of their contextual nuances, operating advantages,functional limitations, quantitative characteristics, and deployment-specific future scopes. This will assist readers to identify functionalmodels that can be applied for their operation-specific use cases. After referring this comparison, readers will be presented with acomparative analysis of these models in terms of their detection accuracy, classification delay, input datasets, computational complexity,and scalability levels. Based on this comparison, readers will be able to identify optimally performing models as per their qualitativecharacteristics.