Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals hesitant to embrace them for critical decision-making. This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. Our contributions include a meticulously designed model, incorporating pivotal techniques such as resampling, data leakage prevention, feature selection, and emphasizing the model’s comprehensibility for healthcare practitioners. This multifaceted approach holds the potential to significantly impact the field of healthcare by offering a reliable and understandable tool for heart stroke prediction. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 distinct attributes. Applying these techniques, including model interpretability measures such as permutation importance and explainability methods like LIME, has achieved impressive results. While permutation importance provides insights into feature importance globally, LIME complements this by offering local and instance-specific explanations. Together, they contribute to a comprehensive understanding of the Artificial Neural Network (ANN) model. The combination of these techniques not only aids in understanding the features that drive overall model performance but also helps in interpreting and validating individual predictions. The ANN model has achieved an outstanding accuracy rate of 95%.

As e-commerce has grown gradually online item assessments have emerged as a key source of consumer data. That stated, there are problems with the consistency and fictitiousness of the evaluations because there are numerous fake or fraudulent ones. These misleading assessments are generated during the investigation in an attempt to mislead customers about the nature of a real advantage, compromising their ability to make a predetermined decision and damaging the reputations of businesses. A cutting-edge interrogation department revealed that quantum machine learning (QML) could manage a huge amount of machine-trained data and could convey almost emotional choices in the context of inaccurate checks. It is truly beneficial in obtaining reviews for things that are incorrect. Opinion, generating trends, interpersonal relationships, and assessing fatigue is merely a few of the attributes. Tests conducted utilizing the Amazon fraudulent review. The dataset demonstrates that QML tactics outperform conventional knowledge acquisition procedures in errands, including the place of fraudulent reviews. The delicacy and tolerance of incorrect review distinguishing evidence can be significantly advanced, although QML is still in its early stages of development. Both our proposed model and model pass rigorous conventional machine learning algorithms testing with a remarkable level of accuracy. An article introduces a unique approach to fraudulent review detection and brings in the QNN algorithm as a solution. A deep learning model, such as this one, has an 86% accuracy rate in quantum computer implementation, which is an impressive level of innovation, especially if it comes with successful results. Involvement in these cutting-edge technologies promises significant benefits in battling the problem of false identities on the Web. In our case, the experimental results demonstrate that our QNN algorithm, which can accurately identify fake reviews, will become a key weapon for suppressing various forms of fraudulence on emerging digital technology platforms.