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Gaussian Noise Removal, Motion Noise Reduction, and Contact Loss Handling (CLH) are used with a Fuzzy Inference System (KL-based FIS) integrated with Kalman filtering during the data preprocessing stage. The state-of-the-art Green Anaconda Optimization (GAO) is used for feature selection, simulating the stages of exploration and exploitation seen in wild green anacondas (GAs). The Multi-Level Group Convolution Light Weight Transformer Network, or LWTNet, which is based on MLGConv, is the foundation of the heart disease classification model. MLGConv is a module that effectively maintains lower computational costs while simultaneously representing multi-level and multi-group features, thereby improving local feature diversity. The Light Former transformer block comes from MLGConv and uses the least processing power to capture global dependencies, resulting in the final LWTNet. The Enhanced PeaFowl Optimization Algorithm (EPFOA) is utilized in the hyperparameter tuning of the classifier model. 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