Reducing False Positivity
False positives are an inherent challenge in screening, as the screening tests often over-diagnose to avoid missing true positive cases. This leads to repeat or second-tier confirmatory tests, adding cost, delays, and anxiety for families. NaviSys employs advanced machine learning methods, such as Random Decision Forests to the large metabolomic data, thereby improving separation of true and false cases. These approaches cuts false positives by 80–90%, reducing unnecessary testing and making screening more accurate, efficient, and patient-friendly.

Diagnostic Decision Support Systems
Multiplex testing allows hundreds of biomarkers to be screened in a single sample, but traditional cut-off-based analysis and one marker for one disease approach relies heavily on expert doctors, making large-scale projects slow and resource-intensive. Complex metabolite correlations are often missed, reducing diagnostic accuracy. Navisys employs machine learning trained over vast data to uncover patterns across multiple biomarkers, discover novel metabolic patterns, and deliver faster, more precise diagnoses than conventional methods. This reduces dependence on scarce specialists, saves costs, and improves turnaround times—critical advantages for large government or public health screening programs

Clinical Decision Support System
Combining clinical and demographic data with test results enables physicians to act quickly and accurately, reducing ineffective treatments and unnecessary referrals. In many of our tests, even doctors may lack clarity on next steps—such as which confirmatory tests to order or the best treatment course. By embedding these insights directly into our reports, we provide clear guidance and a key differentiator. For example, in large-scale government programs for sickle cell anaemia and thalassemia, where hundreds of interpretations are possible, AI automation delivers precise clinical advice, saving costs and empowering doctors, and government decision makers, to make impactful decisions at scale

Advance Metabolomics Workflow
Mass spectrometry-based metabolomics relies on algorithms for preprocessing, peak detection, integration, marker identification, and data analysis—usually provided as generic software by instrument manufacturers. While useful, these tools are not tailored for highly specialized applications like ours. Critical steps still demand rare domain expertise, creating bottlenecks. Navi-Sys our customised, automated software with advanced machine learning capabilities bridges this gap by improving accuracy, reducing reliance on experts, and streamlining the entire workflow. This enables faster, more efficient, and scalable metabolomics analysis, making it practical for unique large-scale screening and diagnostic applications.