Cite this Article

Ishwarya, Archana, 2025. "Personalized Healthcare Diagnostics Using Multi-Source Genomic and Behavioral Data", International Journal of Research in Artificial Intelligence and Data Science(IJRAIDS)1(1): 35-47.

The International Journal of Research in Artificial Intelligence and Data Science (IJRAIDS)
© 2025 by IJRAIDS
Volume 1 Issue 2
Year of Publication : 2025
Authors : Ishwarya, Archana
Doi : XXXX XXXX XXXX

Keywords

Personalized healthcare, precision medicine, genomic data, behavioral data, multi-source data fusion, AI in diagnostics, machine learning in healthcare, predictive analytics, healthcare informatics, patient-centric diagnostics, data-driven healthcare, bioinformatics, electronic health records (EHR), genetic profiling.

Abstract

In the rapidly changing world of modern medicine, a groundbreaking change is happening: personalised healthcare diagnostics that use genomic and behavioural data from many sources. Traditional diagnostic systems, while beneficial, frequently utilise a "one-size-fits-all" approach that does not consider the intricate uniqueness of human biology and lifestyle. We now have the ability to study health at an unprecedented personal level since genome sequencing technology and behavioural data collecting through wearables, cellphones, and digital platforms are becoming more common. This study examines the integration of genomic and behavioural datasets to develop personalised diagnostic systems that can identify, predict, and potentially prevent disease with enhanced accuracy and promptness.

The human genome is the core of this strategy. It is a dynamic map of genetic information that may tell us about disease susceptibility, treatment response, and biological features. Using cutting-edge methods like whole-genome sequencing, CRISPR analysis, and polygenic risk assessment, doctors can now find hidden genetic markers long before any clinical signs show up. Genetics alone, though, does not influence health outcomes. Behavioural patterns, including physical activity, sleep cycles, nutrition, stress, and social contacts, are equally significant. These behavioural insights, obtained by digital phenotyping and sensor-driven monitoring, enhance genomic data by providing context for gene expression in real-world scenarios. The outcome is a more comprehensive and nuanced understanding of personal health.

This research examines the techniques employed to amalgamate these varied data streams. Machine learning (ML), deep learning (DL), and data fusion techniques are the main ways to align, understand, and make conclusions from organised (like genome sequences) and unstructured (like lifestyle logs) data. Predictive algorithms trained on this hybrid data can find the first signs of sickness, tailor treatment regimens, and improve preventive measures with an unequalled level of detail. In oncology, cardiology, and mental health, this method has already shown how it can change things for the better. For example, AI models that look at both genetic predisposition and behavioural risk factors can give early warnings about heart disease or depression, so that steps can be taken before symptoms get bad or permanent.

Nonetheless, the execution of these individualised diagnostic frameworks encounters considerable obstacles. There are big ethical and logistical problems with genomic and behavioural data because of privacy concerns, the lack of standardisation across data sources, and the fact that high-throughput sequencing is not available to everyone. Additionally, incorporating AI-driven insights into conventional clinical procedures necessitates significant transformations in medical education, policy, and infrastructure.

The discipline is ready to grow quickly in the future thanks to the ongoing development of edge computing, wearable biosensors, federated learning, and quantum genomics. Future research is anticipated to broaden into multi-omic integration, real-time health monitoring, and the implementation of dynamic feedback systems for ongoing diagnosis and personalised care. The integration of multi-source data in personalised healthcare diagnostics offers the potential for enhanced medical practices that are not only more effective but also more intelligent, equitable, and humane—tailored to the individual rather than the general population.

Introduction

In a time where algorithms can guess what we'll buy next, what song we'll listen to next, and how to organise our digital lives, a more important concern comes up: can they also guess what sickness we'll have next? Healthcare nowadays is going through a big change. Instead of using standard treatment procedures, doctors are now using very personalised care, where each patient's unique genetic composition and behaviour patterns influence diagnosis and therapy. The integration of genomic information and behavioural data, which were previously separate, is what makes this change happen. Together, they are the foundation of personalised healthcare diagnostics, creating a new way of thinking about medicine that doesn't only treat disease but also predicts it.

Traditional diagnostics have been beneficial for medicine; nevertheless, they frequently depend on symptomatic analysis and population averages, neglecting the intricate biological and behavioural variations among people. A standardised strategy can overlook nuanced indicators that are essential for early detection or tailored treatment techniques. The rise of cheap genome sequencing plus the huge amount of digital behavioural data available from activity trackers, cellphones, and even social media has created a new chance to change the way we diagnose diseases. These varied and ever-changing data sources enable both clinicians and AI models to generate health projections that are not only precise but also customised for each individual.

Genomic data, which is basically the biological script of life, gives us a lot of information about inherited features, how likely someone is to get sick, and how they could respond to different therapies. It sets the stage for predictive and precision medicine, which makes it possible to provide proactive care instead of reactive care. Genes do not function alone. Behavioural research shows that environmental factors, daily behaviours, stress levels, nutrition, and sleep patterns have a big impact on how genes are expressed and how diseases arise. This behavioural layer gives real-time, context-based input on the person's current status and the effects of their lifestyle, bridging the gap between biological potential and experienced reality.

To combine these two areas—genomics and behavior—we need advanced data fusion methods, strong computational models, and ethical guidelines. Machine learning (ML) and deep learning (DL) algorithms, along with modern statistical tools, make it possible to make sense of huge, high-dimensional datasets. These models may discover connections, find risk profiles, and recommend personalised diagnostic and therapeutic techniques that improve results and efficiency by training on integrated datasets.

This paper examines the evolving domain of personalised diagnostics that utilise both genomic and behavioural data. It looks at how data is collected and combined, the machine learning models that make this revolution possible, and how it might be used in many areas of medicine. It also talks about the important ethical, social, and technical problems that need to be solved, such as data protection, fair access, and problems in putting the plan into action in the clinic.

As healthcare changes to keep up with the needs of 21st-century medicine, combining data from many sources will be important for more than just better diagnostics; it will also be important for changing the whole healthcare experience. The future of medicine is not simply personal; it is also predictive, preventive, and heavily reliant on data. In this future, diagnostics won't only tell us what's wrong; they'll also tell us what's coming and how to fix it.