Stroke is one of the fatal brain diseases that cause death in 3 to 10 hours. However, most stroke mortality can be prevented by identifying the nature of the stroke and reacting to it promptly through smart health systems. In this paper, a machine learning model is approached for predicting the existence of stroke of a patient where the Random forest classifier outperforms the state-of-the-art models, including Logistic Regression, Decision Tree Classifier (DTC), K-NN. We conduct the experiments on datasets which has 5110 observations with 12 attributes. We also applied EDA for preprocessing and feature techniques for balancing the datasets. Finally, a cloud-based mobile app collects user data to analyze and provide the possibility of stroke for alerting the person with the accuracy of precision 96%, recall 96%, and F1-score 96%. This user-friendly system can be a lifesaver as the person gets an essential warning very easily by providing very little information from anywhere with a mobile device.

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Aim: Clustering belongs to unsupervised learning, which divides the data objects in the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity. Background: The clustering of spatial data objects can be solved by optimization based on the clustering objective function. Objective: Study on Intelligent Analysis and Processing Technology of Computer Big Data Based on Clustering Algorithm. Method: First, a new dynamic self-organizing feature mapping model is proposed, and the training algorithm of the model is given. Then, the spectral clustering technology and related concepts are introduced. The spectral clustering algorithm is studied and analyzed, and a spectral clustering algorithm that automatically determines the number of clusters is proposed. Furthermore, an algorithm for constructing a discrete Morse function to find the optimal solution is proposed, proving that the constructed function is the optimal discrete Morse function. At the same time, two optimization models based on the discrete Morse theory are constructed. Finally, the optimization model based on discrete Morse theory is applied to cluster analysis, and a density clustering algorithm based on discrete Morse optimization model is proposed. Results: This study is focused on designing and implementing partitional based clustering algorithm based on big data, that is suitable for clustering huge datasets to meet low computational requirements. The experiments are conducted in terms of time and space complexity and it is observed that the measure of clustering quality and the run time is capable of running in very less time without negotiating the quality of clustering. The results show that the experiments are carried out on the artificial data set and the UCI data set. Conclusion: Efficiency and superiority of the new model are verified by comparing with the clustering results of the DBSCAN algorithm.

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