# Probability Analysis of Hypertension-Related Symptoms Based on XGBoost and Clustering Algorithm
## 4 Conclusions  
In this paper, we first pretreated hypertensive data and extracted principal components. After labeling patients with clustering algorithm, combined with XGBoost, the probability of different symptoms of patients was calculated. The final results show that hypertension patients have a high probability of vascular sclerosis symptoms. In other symptoms, there are significant differences between the two groups. The second group has a greater probability of ventricular hypertrophy and microalbuminuria symptoms than the first group. Therefore, if a hypertensive patient is divided into the second type of hypertensive patients, we can focus on the prevention and treatment of microalbuminuria and ventricular hypertrophy. Hypertension can damage organs, which is painful for patients. Microalbumin is the most sensitive and reliable diagnostic indicator for early detection of nephropathy, and ventricular hypertrophy is a precursor of heart disease. Therefore, preventive treatment of type II hypertension patients can reduce the damage of hypertension to the kidney and heart. For a new patient, classification can be determined by the clustering method in this paper. If many patient categories have been determined by this clustering method, these patients can be added as sample points to the original sample and the probability of different symptoms recalculated. As time goes by, the sample will continue to increase and the final probability will be more accurate. At present, young people with hypertension in China are increasing year by year, and obesity is becoming more common among young people, meaning that within a short time the condition of hypertension in China will further deteriorate. Hypertension itself is not a major threat, but it will \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **1** & \multicolumn{2}{c}{**2**} \\ \hline
**XGBoost Prediction Result** & \multicolumn{3}{c}{**98.48\%**} \\ \hline
**Symptom** & **Ratio** & **Probability** & **Ratio** & **Probability** \\ \hline Ventricular Hypertrophy & 10.10\% & 9.95\% & 34.90\% & 34.37\% \\ Vascular Sclerosis & 89.10\% & 87.75\% & 96.40\% & 94.93\% \\ Lower Limb Ischemia & 2.20\% & 2.17\% & 0.80\% & 0.79\% \\ Renal Insufficiency & 0.70\% & 0.69\% & 4.10\% & 4.04\% \\ Microalbuminuria & 7.20\% & 7.09\% & 41.20\% & 40.57\% \\ Fundus Disease & 0.70\% & 0.69\% & 3.30\% & 3.25\% \\ Stroke & 5.80\% & 5.71\% & 6.40\% & 6.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Clustering and XGBoost methods for predicting symptom probability summary table. Figure 5: XGboost node graph. cause damage to target organs. For patients, if the target organ damage occurs, it will not only have a great financial burden, but also have a great impact on the body and spirit. In conclusion, the analysis of hypertension-related symptoms and target organ damage is urgent. The method proposed in this paper can obtain the probability of certain symptoms in different types of hypertensive patients, and it can guide different types of hypertensive patients for targeted treatment, greatly reducing the risk of suffering from more serious diseases, with strong practical significance. With the continuous advancement of the era of big data and the continuous development of the medical field, more and more medical data will be preserved. Utilizing big data analysis methods to scientifically use these saved data to increase the accuracy of medical diagnosis will be an important research direction in the future. This paper combines big data analysis methods with a hospital's data, and successfully applies big data analysis methods to the medical field. This method can predict the probability of some related symptoms in hypertensive patients. In the medical field, big data analysis can play a huge role, but the extensive application of data analysis in the medical field requires a lot of research. With the continuous development of big data analysis, more accurate research results will make outstanding contributions in the medical field.