Publication

Abstract : The development of mobile applications for diagnosing nutritional deficiencies and diseases represents a significant advancement in healthcare, particularly for maternal and child health. These digital tools offer a convenient, accessible, and data-driven approach to monitoring health parameters. Two mobile applications, Symptom Checker and Food Group, were developed using Flutter and a PHP-MySQL server as the backend, available in both English and Tamil languages. These applications allow the recording of Anthropometric, Biochemical, and Clinical Parameters for individuals, facilitating the identification of nutritional deficiencies. Data from 799 women and 286 children were collected using the apps, generating long-term big data sets that can predict potential nutritional deficiencies. The Symptom Checker app tracks parameters such as Gender, Age, Height, Weight, Body fat, Mid-arm circumference, and Hemoglobin levels, providing insights into nutritional health. Data is stored monthly and can be analysed over six-month intervals, while clinical symptoms related to skin, teeth, mouth, eyes, hair, nails, and general appearance help detect deficiencies like protein, iron, Bcomplex, and iodine, along with obesity and lean body mass issues. The Food Group app records daily food intake across 11 ICMR-recommended food groups, offering a detailed assessment of diet diversity. Over a six month period, the analysed dataset shows an alarming status of iron deficiency anaemia of about 67% in women and 52% in children. A majority of users (57% of women and 49% of children) reported intake from fewer than 3 (mothers) and 4 (children) food groups daily, resulting in lower levels of essential nutrients such as proteins, iron, and vitamins. Correlation analysis between the diet diversity scores and clinical symptoms recorded in the Symptom Checker app reveals a strong relationship (r = 0.68, p < 0.01) between limited diet diversity and deficiencies in iron, Bcomplex vitamins, and iodine. By linking the data from both applications, a correlation between diet diversity and nutritional deficiency symptoms is established. Statistical analysis of this data provides significant results, helping to identify nutritional deficiencies due to limited diet diversity, contributing to data-driven decisions and more targeted nutritional interventions in the community.