# Child's Target Height Prediction Evolution
## 2 State-of-the-Art  
As discussed in the previous chapter, the height assessment on a child assumes critical importance when improving child health and wellbeing.  
Prediction for adult height can be used both for diagnosis or following treatments and provides a valuable tool in pediatrics. For instance, it can help detect growth hormone deficiency with hGH and follow treatment performance. In diagnosis, the height of the child can be compared with the so-called \"target height\", by considering the parent's height and child's gender [5].  
There are two known methods for assessing the \"target height\".  
[PERSON] has defined the following model to forecast the height (when adult) for a child [17]:  
\[\textit{Female height (inches)}=+\frac{\textit{father height \times 12/13}+\textit{mother height}}{2} \tag{1}\]  
\[\textit{Male height (inches)}=+\frac{\textit{father height + mother height \times 12/13}}{2} \tag{2}\]  
Other common used model is the Mid-Parental Method (described by [PERSON]) [18,19]:  
\[\textit{Female height (inches)}=+\frac{\textit{father height + mother height}}{2}-2.5 \tag{3}\]  
\[\textit{Male height (inches)}=+\frac{\textit{father height + mother height}}{2}+2.5 \tag{4}\]  
There is a lot of information regarding the applicability of the methods, but information about the studies' bases and characteristics are scarce, probably because they have been performed some decades ago. Still, it was possible to comprehend that both methods are based on statistics approaches where a simple regression method is applied to the population and the \"optimal\" formula is defined. In a generic way, these approaches try to find the mathematical formula that best fits the general characteristics of the population.  
Horace grey approach reports to a study published in 1948 and is based on 53 collected cases. This method introduces a different weight on the mother's or father's height when calculating the output prediction. It emphasizes that a mother and father's influence is different for different child's genders [17].  
The method described by [PERSON] was introduced in 1970 and has been a standard procedure for assessing children's growth since. It was not possible to find a description of the dataset used in the study, but it was still possible to understand that this method approaches the height of parents on the same proportion, given the emphasis on children's gender, which is critical to defining height [20].  
As we observed, the methods did not introduce personalization to the individual level, besides the influence of gender, which is one of the advantages that machine learning can introduce in these areas of study.  
There are other studies besides parent's height can use bone age, actual age, or child's current height. All these studies were based on longitudinal population projects [21,22].  
These projects, also called cohort studies, involve observing and measuring population variables (the same individuals-cohort) for long periods, which can involve years or decades.  
In the current study, we will only focus on information gathered at childbirth, and as far as we know, the two methods described before are the only existing \"tools\" for predicting a child's height when adults. They are quite popular and it is easy to find applications of these methods in several child development areas.  
We have also found research on applications of machine learning methods that address \"target\" height prediction. We did not find studies addressing this subject.  
Knowing that this subject was never approached on this perspective represents, in our view, an opportunity, and was one of the main reasons for proceeding with this work. This study develops anew method in forecasting children's target height using a completely different approach based on machine learning tools.