• Vollala Viha
• Goranti Ramya
• Padmawar Srija
• Ch Gopi

Machine learning has emerged as a driving force in both scientific research and industrial applications, particularly in the context of handling vast amounts of data known as Big Data. With the availability of extensive healthcare datasets and continuous advancements in machine learning techniques, computers have become adept at accurately diagnosing various medical conditions. This study aims to address the pressing issues surrounding anxiety and depression in pregnant women by employing performance-optimized algorithms to extract pertinent features. The objective is to expedite the process and minimize the number of inquiries required to yield meaningful results. Building upon this, the research endeavors to develop an instantaneous remote health status prediction system tailored for assessing depression and anxiety in pregnant women. Leveraging the Apache Spark Big Data processing engine, which specializes in handling streaming Big Data, the scalable system acquires data from pregnant individuals to forecast their health condition. Subsequently, the system applies the Naïve Bayes machine learning algorithm, which has demonstrated superior performance on the dataset, achieving an accuracy of 90.8% and a precision of 81.71%. Through the integration of this Big Data platform, the traditionally time-consuming procedure of detecting anxiety and depression in pregnant women can be replaced by a computer-based technique that operates swiftly while maintaining a respectable level of accuracy.

Big Data Analytics Framework , Perinatal Mental Health , Machine Learning Techniques , Depression and Anxiety Disorders , Feature Selection , Hybrid Machine Learning , Scalable Big Data Platform , Rapid Disease Diagnosis

"Development of an artificial intelligence-supported hybrid data management platform for monitoring depression and anxiety symptoms in the perinatal period", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.j484-j488, April-2024, Available :http://www.jetir.org/papers/JETIR2404963.pdf

"Development of an artificial intelligence-supported hybrid data management platform for monitoring depression and anxiety symptoms in the perinatal period", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppj484-j488, April-2024, Available at : http://www.jetir.org/papers/JETIR2404963.pdf