You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review 
Chapter Title:  K-Nearest Neighbour (KNN) 
1. Abstract of Cited Paper (BIB001):  Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.

Literature Review:
Classification algorithms can be divided into lazy learners and eager learners. Lazy learners simply store the training data and do not build any models. They wait until a sample is provided for the classification. Eager learners construct a classification model using the training data and use the model for classification. Lazy learners are relatively slow during prediction. KNN is a lazy learning algorithm. Given a data sample, it would find K number of nearest neighbours from the training set and target class of the given sample will be decided based on the most common target class of the neighbours (Peterson, 2009 ). Among the proposed machine learning based ASD diagnosis approaches, KNN was used in BIB002 BIB001 .