Health-Lens: A Health Diagnosis Companion - Chandrakala C.B; S., Pooja; Pujari, Chetana; Ketavarapu, Sriyans; Awatramani, Sahil; Gohil, Siddharth (International Journal of Interactive Mobile Technologies (iJIM), 2025)
The “Health Lens” application represents a transformative approach to healthcare, leveraging advanced machine learning to enhance accessibility and diagnostic accuracy in dermatology, especially in underserved regions. This abstract outlines the study’s key findings and implications, structured to enhance clarity and provide depth. Machine Learning Model’s Performance: The core of the application is a robust machine learning model trained on the ISIC 2019 dataset [72], achieving an accuracy of 92%, with a precision of 89% and a recall of 90%. These metrics indicate superior performance compared to baseline methods, establishing the efficacy of the model in the diagnosis of skin conditions. Gender Distribution & Localization: Analysis revealed a higher prevalence of certain skin conditions among men, likely influenced by occupational and lifestyle factors. Conditions such as basal cell carcinoma were predominantly localized in body parts exposed to UV radiation, underscoring the need for targeted health interventions. Potential Overfitting & Mitigation Strategies: Initial model tests indicated potential overfitting, addressed through techniques such as dropout and cross-validation during training. This adjustment ensured the robustness of the model, making it reliable for practical use. Application Features & Impact: “Health Lens” is distinguished by its user-friendly interface and real-time diagnostic capabilities, which significantly reduce barriers to accessing dermatological care. The application also supports sustainable healthcare practices, aligning with the Sustainable Development Goals, particularly in promoting good health and reducing inequalities. Limitations & Future Directions: The study acknowledges limitations such as reliance on a singular dataset and potential connectivity problems in remote areas. Future developments will focus on integrating more diverse datasets and expanding the range of conditions covered, enhancing both the accuracy and utility of the application.