An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Abstract

Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. The multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. The assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. The enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclassification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. The model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial prediction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease.

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

“…However, due to the lack of studies with large sample sizes and in absence of a clear consensus, it is difficult to select a suitable model. Therefore, it is necessary to comprehensively analyze different algorithms and hyper-parameters to find the optimal model [ 69 ], and meanwhile consider the precision, complexity and training time of the model. Finally, the performance of the selected model can be assessed by the techniques such as cross-verification, so as to ensure its best efficiency in practical application [ 70 ].…”

Perspective Chapter: Macroeconomic Dynamics through the Lens of the Adaptive Neuro-Fuzzy Inference System

“…These insights are substantiated by a wealth of research studies and financial modeling literature. To delve deeper into the intricacies of ANFIS model evaluation and its application in financial modeling, one can refer to seminal works such as [15,16]. Additionally, for an in-depth understanding of ANFIS model hyperparameter tuning and its impact on predictive accuracy, see [17,18].…”

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