Diabetes, a chronic condition, affects numerous populations. Poor insulin production from the pancreas combined with high blood sugar levels can result in the onset of diabetes. Diabetes can be caused by numerous factors. Observe and prevent these factors to reduce the high prevalence of diabetes. This study concentrates on medical record data for determining diabetes risk factors via statistical correlation analysis. These factors will be utilized as machine learning and LSTM input parameters for diabetes prediction. The factors analyzed include blood glucose levels, HbA1c levels, age, BMI, hypertension, heart disease, smoking habits, and gender. Based on the research results, we found that glucose levels (>137 mg/dL) and HbA1c levels (>6.5%) are the main benchmarks in diagnosing diabetes. It is also supported by the correlation value, which is relatively high (0.42 and 0.40, respectively) compared to other factors. Increasing age and BMI also increase the risk of developing diabetes. Comorbidities (such as hypertension or heart disease) and smoking habits can worsen the condition of people with diabetes. Meanwhile (based on gender), women are more at risk of developing diabetes than men because their body mass index increases during the monthly cycle. Apart from that, there is a tendency for blood sugar levels in women to increase in the last two weeks before menstruation. Based on the prediction results, the highest levels of accuracy, sensitivity, and F1 score were obtained (96.97%, 99.97%, and 98.37%) using the LSTM method. This performance shows that LSTM is relatively good for the diabetes prediction process based on existing factors/parameters.

Sexually transmitted infections (STIs) remain a major global public health concern, with more than one million new cases acquired daily and increasing antimicrobial resistance compromising effective control strategies. Rapid, accurate and multiplex molecular diagnostics are therefore essential to support timely clinical management and public health surveillance. This study evaluated the analytical and clinical performance of the cartridge-based STANDARD™ M10 STI Panel (SD Biosensor, Republic of Korea), a fully automated, random-access real-time PCR assay capable of detecting eight STI-related pathogens within approximately 64 min. A total of 150 residual clinical specimens were retrospectively analysed, including vaginal, rectal, urethral and oropharyngeal swabs, as well as seminal fluids, and results were compared with those obtained using the Allplex™ STI Essential Assay (Seegene, Republic of Korea), which shares six common targets. The STANDARD™ M10 STI Panel demonstrated high diagnostic accuracy, with sensitivity ranging from 89.5% to 100%, specificity from 98.2% to 100% and overall accuracy between 98% and 100%. Agreement between the two assays was almost perfect, with Cohen’s κ values ranging from 0.91 to 1.00. Analytical sensitivity was further confirmed through verification of the limits of detection using quantified reference standards. Although validated for urine samples, the assay also showed robust performance on alternative clinical matrices, particularly vaginal swabs. Overall, these findings indicate that the STANDARD™ M10 STI Panel represents a reliable and practical tool for STI diagnosis, combining rapid turnaround time, minimal hands-on requirements and broad pathogen coverage in both centralized and near-patient testing settings.