Hardik Joshi

Diabetes prediction models often suffer from limited generalizability due to reliance on singlepopulation datasets, which fail to capture the diversity of real-world patient demographics. This limitation reduces their clinical applicability across different ethnic groups and geographic regions. The research aims to improve diabetes prediction accuracy and generalizability by combining multiple datasets and employing ensemble learning techniques, addressing the challenges of imbalanced data and population diversity. The research combines two publicly available datasets (Pima Indians: 768 samples and German Society: 2,000 samples) and utilizes preprocessing procedures conducted on these datasets. By comparing the performance of the individual dataset (Pima Indians and German Society datasets) and the combined dataset, it is clear that the models trained on the combined data show improved performance on all metrics. The Random Forest model outperforms the other ensemble models in the Pima Indians dataset, achieving an accuracy of 0.817. The models with the highest accuracy on the German Society dataset are Gradient Boosting and Random Forest, with respective accuracies of 0.996 and 0.994. Then, in the combined dataset, Gradient Boosting and Random Forest yield the best accuracy of 0.991 and 0.988, respectively. It is noticeable that this improvement reflects the ability of models trained on combined data to better accommodate diversity in the data, allowing them to generalize more effectively when applied to different populations. Future research should explore deep learning techniques and additional diverse datasets to enhance model performance further.

Today, Artificial Intelligence (AI) has become an integral part of our day-to-day life. From personal task to professional life everywhere we need AI. All the sectors like - transport, education, healthcare, agriculture, we find AI everywhere. Each coin has two side, similarly AI has its own pros and cons. Main challenge with AI is ethics. There is no doubt in the work efficiency of AI but ultimately, it’s a machine without emotions hence whatever the decision it takes it purely without ethical values. Sometimes, this type of decision may lead to disasters, especially in the industry where human life is involved e.g. healthcare. The focus of this paper is to integrate moral values into AI systems. It also talks about different ethical frameworks like utilitarianism, deontology and virtue ethics along with the state-of-art work and knowledge gaps. This research also explored various case studies where AI implemented with ethics. Integration of moral values with AI has many issues like bias, transparency and accountability. Here author has proposed a new model named Ethical Alignment Algorithm (EAA). This model helps to integrate ethics with AI step-by-step. This approach will help AI to make fair, sensible and responsible decisions. This paper will also help researchers to work with a multidisciplinary approach. Different subject specialists can come together and make AI policies with ethics. EAA has the potential to make the AI systems not only advanced but with high moral values. In the end, the paper highlights current AI development and future scopes. The main aim of this research is to promote justice and fairness in AI decisions for the overall well-being of society.

Diabetes is an illness that is widespread throughout the world and is considered a health concern, which requires work to explore advanced predictive techniques for early diagnosis of the illness. This paper discusses diabetes prediction by using the K-Nearest Neighbors (KNN) classifier, which is a widely used algorithm in machine learning. Most studies only dealt with investigating the optimal value of k in the KNN algorithm and did not address the best method to measure distance alone or together with the optimal value of k to improve the efficiency of diabetes prediction. This study simultaneously investigates both the optimal value of k and the optimal method for measuring distance to improve the performance of the KNN technique in predicting diabetes. By using and analyzing the Indian Diabetes PIMA dataset, this study seeks to discover the extent to which different parameters, especially the optimal value of K and distance metrics, affect the performance of the classifier. Through experiments that included applying different values for the K factor and using various distance measures, the study reached insights into maximizing the classifier's accuracy. The study shows that choosing the distance measure greatly affects the accuracy of classification and selecting the optimal K value helps eliminate problems of overfitting and underfitting, which is a feature of robust models for diabetes prediction. The research results showed that the best performance achieved was 80.5% when ????=35 and the Euclidean distance measure was used.