Blockchain Associated Machine Learning Approach for Earlier Prognosis and Preclusion of Osteoporosis in Elderly

Osteoporosis (OP), or porous bone, is a severe illness wherein an individual's bones weaken, increasing the likelihood of fractures. OP is caused by micro-architectural degradation of bone tissues, which raises the probability of bone fragility and can result in bone fractures even when no force is placed on it. Estimating bone mineral density (BMD) is a prevalent method for detecting OP. For women who have reached menopause, prompt and precise forecasts and preventative measures of OP are essential. BMD can be measured using imaging methods like Computed Tomography (CT) and Dual Energy X-ray Absorptiometry (DEXA/DXA). Blockchain (BC) is a revolutionary technique utilized in the health sector to store and share patient information between clinics, testing centres, dispensaries, and practitioners. The application of Blockchain could detect drastic and even serious errors. As an outcome, it may improve the confidentiality and accessibility of medical information interchange in the medical field. This system helps health organizations raise awareness and enhance the evaluation of health records. By integrating blockchain technology with machine learning algorithms, various bone ailments, including osteoporosis and osteoarthritis, can be identified earlier, which delivers a report regarding the prediction of fracture risk. The developed system can assist physicians and radiologists in making more rapid and better diagnoses of the affected ones. In this work, we developed a completely automated mechanism for suspicious osteoporosis patients that uses machine learning techniques to improve prognosis and precision via different processes. Here, we developed a computerized system that effectively integrates principal component analysis (PCA) with the weighted k-nearest neighbours algorithm (wkNN) to identify, predict, and classify the BMD scores as usual, osteopenia, and osteoporosis. The ranked results are validated with the DEXA scan results and by the clinicians to demonstrate the efficacy of the machine learning techniques. The laboratories use BC to safely and anonymously share the findings with the patients and doctors.