# MGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases
## 5 Conclusions  
Focusing on the topic of quality improvement of food composition databases, we present a methodology which can improve the quality of existing FCDBs. We decided to tackle one of the most common and quickly growing problems in FCDBs, which is missing food composition data (FCD). Our proposed methodology, MIGHT, deals with the incomplete coverage of foods or nutrients leading to missing data by introducing rules for borrowing data for imputation of missing values from other FCDBs, generated with modeling based on null hypothesis testing. We explore the idea that proper statistical analysis can decrease the error of borrowing data for imputation of missing values in FCDBs. The null hypothesis for testing is set to be \"There is no effect difference between the separate datasets\", the datasets being the separate national FCDBs that can possibly borrow data from each other. On the collected data, adequate statistical analysis are performed for testing the null hypothesis. After that, based on the post-hoc procedure used, our methodology goes in two directions: MIGHTv1 uses All vs. all post-hoc procedure and MIGHTv2 uses One vs. all post-hoc procedure. After the post-hoc procedure application, the rules for borrowing are generated. These rules are generated as many times as there are instances in the set used for the experiments. The final rules are a result of summarizing the rules generated by all models. When the final rules are generated, we introduce measures for calculating the value by using the rules. Our measures of choice are average and median, because of the fact that current techniques for borrowing data for imputation of missing FCD most commonly involve these two measures for calculating the nutrient value that has to be borrowed. The end result for a missing value of a specific nutrient in a food from a given country is a value obtained from a set of countries whose FCDBs are eligible for borrowing. For future work, we plan to investigate the properties of the proposed methodology by performing sensitivity analysis using simulated data with known characteristics. We also plan to extend the \begin{table}
\begin{tabular}{c c c} \hline
**Use Case** & **Execution Time MIGHTv1** & **Execution Time MIGHTv2** \\ \hline Potassium (K) in Fruits & 4.5 s & 4.3 s \\ Sodium (Na) in Fruits & 3.9 s & 3.9 s \\ Sodium (Na) in Vegetables & 3.9 s & 3.6 s \\ Protein (PROT) in Meat & 2.5 s & 3.0 s \\ \hline \end{tabular}
\end{table} 
Table 13: Execution times for obtaining rules for all use cases. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Food/Country** & **IT** & **UK** & **CH** & **BE** & **DK** & **SI** & **NL** & **USA** & **CA** \\ \hline Beet root & 10 & 66 & 58 & 58 & 43 & 58 & 70 & 78 & 77 \\ : & : & : & : & : & : & : & : & : \\ Spinach & 100 & 140 & 65 & 105 & 41 & 60 & 13 & 79 & 24 \\ \hline \end{tabular}
\end{table} 
Table 14: Big variations in the data for nutrient values of Sodium (Na) in foods from the food group “Vegetables”. methodology to work for data imputation of more than one missing value using multivariate statistical analysis and some modification of the NMF method. On the topic of missing FCD, one of our next steps is also considering other solutions for missing data--calculating missing data from recipes [16] or estimating values from a similar food or the same food in different form. In terms of improving MIGHT, we are planning to work on an extension which will handle situations when more than one nutrient value is missing, following the idea of non-negative matrix factorization in a combination with multi-view learning [57; 58]. [PERSON] and [PERSON] proposed and designed the methodology for missing value imputation using modeling based on _null hypothesis_ testing. [PERSON] and [PERSON] designed and preformed the experiments, and analyzed the data. P.K. and B.K.S. helped directly in the study. B.K.S. contributed data and materials. [PERSON] and [PERSON] wrote the paper. B.K.S contributed to the manuscript.