Early diagnosis of children’s health problems helps the professionals to treat it at an earlier stage and improves their quality of life. The life skills of the young minds are the best investment to build the healthy society of the Country. Researchers across various countries are interested to predict health problems related to children with respect to parenting style, hereditary health issues, food habits and physical activities. Machine learning plays a vital role in analyzing and predicting the hidden facts in the data we collected. The main objective of this study is to present an overview of many machine learning techniques such as Support Vector Machines, Naive Bayes classifier, K-Nearest Neighbor, Decision Tree, K-means algorithm and perform a comparative analysis of their accuracy and help the researchers to choose best algorithm on prediction of Children’s various health problems such as Early Childhood Obesity, Anxiety Disorders, Attention Deficit Hyperactive Disorder, Mental Health Problems, Child Post Traumatic Stress, Autism Spectrum Disorder and Insulin Resistance in Children. This survey paper can lead to develop innovative and efficient algorithms on prediction of children’s health problems to improve their quality of life in a better way.

Machine Learning Algorithms, Prediction, Children’s Health Problems

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