Liver Disease (LD) is the main cause of death worldwide, affecting a large number of people. A variety of factors affect the liver, resulting in this disease. The diagnosis of this condition is both expensive and time-consuming. Machine Learning offers a lot of potential in terms of automated disease diagnosis. As a result, the purpose of this research is to assess the efficacy of various Machine Learning (ML) algorithms to lower the high cost of liver disease diagnosis through prediction. With the current rise in numerous liver disorders, it’s more important than ever to detect liver disease early on. This research proposed intelligent model to predict liver disease using machine learning technique. This proposed model is more effective and comprehensive in terms of performance of 0.884 accuracy, and 0.116 miss-rate.

Energy is fundamental to all significant human endeavors and is crucial for sustaining life and realizing human potential. With the advent of smart homes, energy consumption is increasing as new technologies are introduced, leading to shifts in both lifestyle and societal norms. This scenario presents a unique energy challenge that requires extraordinary efforts to meet the anticipated energy demands. Various innovative strategies are being implemented to overcome the drawbacks and address the growing consumer demand for energy. Today, smart homes offer much more than just basic functions; they also focus on resource management, energy efficiency, and enhancing quality of life. Machine Learning (ML) plays a vital role in smart homes as it allows for the training, adjustment, and optimization of various functions. This intelligent, purposeful capacity has the potential to turn homes into dynamic and practical environments that improve daily performance, ease, and personalization. In this research, an ML-based multivariate model is proposed utilizing Long Short-Term Memory (LSTM) for smart homes, aiming to optimize energy utilization and improve management in the realm of energy consumption. This model offers precise predictions of energy consumption, ensuring minimal random errors. Prominent metrics include a low Mean Squared Error (MSE) of 0.02284, a high Mean Absolute Error (MAE) of 0.184, a Mean Absolute Percentage Error (MAPE) of 0.123, the lowest Root Mean Squared Error (RMSE) at 0.15113, a significant Mean Absolute Scaled Error (MASE) of 0.996, and a strong R-squared value (R²) of 0.694. The proposed model delivers exceptional predictive performance as compared to the previous approaches, ensuring high reliability, which aligns with the standards needed for advancing toward a smart and sustainable future.

The worldwide health epidemic of anaemia which is a condition with low levels of red blood cells or haemoglobin requires accurate prediction models to act promptly and improve patient outcomes because it is widespread and has different causes. The effective management of anaemia is piled with obstructions, which may include the variability of diagnostic criteria, the resource limitations of healthcare, and the multifactorial nature of the disease including nutritional deficiencies, chronic disease, and genetic factors. Conventional anaemia prediction models, that predominantly rely on statistics and are trained on clinical risk scores, are frequently incapable of providing practical solutions and meaningful insights into anaemia diagnosis. There is a growing interest to focus on Artificial Intelligence (AI) use for anaemia prediction, however, traditional AI models (black boxes) lack transparency, which causes doctors not to pick them up for practical usage. Actionable insights that are enabled by transparent AI models (white boxes) based on the explainable AI methodologies reveal the rationales of the prediction, clarify the features that are responsible for them, and provide assistance to clinicians and healthcare providers. In this research work, a transparent anaemia prediction model (white box) empowered by explainable AI techniques is proposed to address the limitations of black boxes in terms of transparency. The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. With the integration of explainable AI techniques like SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME), the proposed model offers insights into the underlying factors influencing anaemia predictions. The proposed model, significantly, represents exceptional growth in the healthcare sector and helps in bridging the gap between predictive performance and clinical interpretability, thus improving patient care and disease management strategies. The model simulation results are showing promising results in terms of the accuracy (98.13%) and the miss-rate (1.87%) which are the superior performance compared to the previous published approaches.

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.