N. Raghavendra Sai

The chronic metabolic disorder known as diabetes mellitus, which is defined by hyperglycemia, poses a significant threat to the health of people all over the world. The categorization is broken down into two primary categories: Type 1 and Type 2, with each category having its own unique causes and approaches to treatment. It is very necessary for the effective management of illnesses to have both the prompt detection and the exact prediction of outcomes. The applications of machine learning and data mining are becoming increasingly important as tools in this setting. The current research study analyses the usage of machine learning models, specifically Voting Ensembles, for the goal of predicting diabetes. Specifically, the researchers were interested in how accurate these models were. Using GridSearchCV, the Voting Ensemble, which consists of LightGBM, XGBoost, and AdaBoost, is fine-tuned to manage outliers. This may be done with or without the Interquartile Range (IQR) pre-processing. The results of a comparative analysis of performance, which is carried out, illustrate the benefits that are linked with outlier management. According to the findings, the Voting Ensemble model, when paired with IQR pre-processing, possesses greater accuracy, precision, and AUC score, which makes it more acceptable for predicting diabetes. Despite this, the strategy that does not use the IQR continues to be a workable and reasonable alternative. The current study emphasizes both the significance of outlier management within the area of healthcare analytics and the effect of data preparation procedures on the accuracy of prediction models. Both of these topics are brought up because of the relevance of the current work.

The primary objective of this research endeavour is to concentrate on the timely detection and prognostication of diabetes and Parkinson's disease through the utilisation of machine learning techniques, specifically the integration of Ant Colony Optimisation (ACO) with the XGBoost algorithm (ACXG). The healthcare issues presented by diabetes and Parkinson's disease underscore the criticality of early detection in order to facilitate effective intervention and enhance patient outcomes. The objective of this work is to establish a connection between the prediction of diabetes and the classification of Parkinson's disease, thereby developing a comprehensive model that improves the prognosis and prevention of these diseases. The project entails the collection and pre-processing of pertinent datasets, afterwards employing a range of classification approaches such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and the innovative ACO-XGBoost model. The results of performance comparisons demonstrate that ACO-XGBoost has superior performance in contrast to conventional approaches. It achieves notable levels of accuracy, precision, recall, F1-score, and AUC, hence establishing its significance as a valuable tool for disease prediction. The incorporation of Ant Colony Optimisation (ACO) with XGBoost (ACXG) showcases the capacity to augment predictive precision and sensitivity, presenting notable progressions in healthcare methodologies. The present study makes a valuable contribution to the advancement of more accurate predictive models, ultimately enhancing the quality of patient care and public health outcomes.

In this article, we have proposed a strategy for applying blockchain innovation to clinical data and using it for PHR applications. Here, we bring construction