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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 these effects, 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 are highly variant in terms of their internal operating characteristics, which makes it ambiguous for clinical designers to identify optimal models for their deployments. Furthermore, these models also showcase highly variant performance levels as per their input feature sets. Thus, it is difficult to identify optimal models for a given set of input parameters. To overcome these issues, this paper initially reviews 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 functional models that can be applied for their operation-specific use cases. After referring this comparison, readers will be presented with a comparative 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 qualitative characteristics. [ABSTRACT FROM AUTHOR]

The work presented in this paper is focused on extracting the tumor region from Brain MRI images and then classifying them as benign or malignant. The first part comprises detection of tumor part using histogram based multi-component and multilevel adaptive thresholding technique and the later includes tumor and non-tumor part features to classify the MRI images in two classes. The multi-component approach uses all the three components other than the approximation component of wavelet decomposed image for thresholding and the multi-level approach includes two level wavelet decomposition of the original gray level MRI image. The 6-twodimensional component values at both the levels are transformed and histogram corresponding to each component are down sampled to level 5 using 1D wavelet transform. The Global minimal value at level 5 is the suitably used as a threshold value to filter each individual component and the new image is then reconstructed. Finally, a mean threshold is used to segment the tumor part from the MRI image. The classification using three different classifiers is based on features extracted from the tumor and non-tumor part. Out of 77 normal and 143 affected images, the proposed technique was able to classify the images with greater accuracy. [ABSTRACT FROM AUTHOR]