## 7 Discussion  
Heart failure remains one of the most important public health problems, a major cause of death and readmission, with an increasing incidence and prognosis most often unfavorable that requires new therapeutic options suitable for each subgroup of patients. Various mechanisms contribute to the development of heart failure with reduced ejection fraction, many of which are underinvestigated. Obesity, hypertension, sedentary lifestyle and metabolic syndrome have been identified as important risk factors for various types of heart disease, including heart failure with reduced ejection fraction. Hospitalizations remain relevant as a countable cost and as a marker for disease severity, quality of life and prognosis. In 2012, [PERSON] et al. evaluated the annual global heart failure burden from all published sources and estimated it at $108 billion annually. The mean immediate HF burden value for the high-income countries was 1.42% versus  
Figure 8: Shapley values for variables related to diabetes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Logit Regression Result**} \\ \hline
**Dependent Variable** & LVEF\_over\_50 & **No. Observations:** & 201 \\ \hline
**Model:** & Logit & **Df Residuals:** & 197 \\ \hline
**Method:** & MLE & **Df Model:** & 3 \\ \hline
**Date:** & Wednesday, 13 July 2022 & **Pseudo R-squ.:** & 0.07059 \\ \hline
**Time:** & 19:22:30 & **Log-Likelihood:** & \(-\)129.43 \\ \hline
**Converged:** & True & **LL-Null:** & \(-\)139.26 \\ \hline
**Covariance Type:** & nonrobust & **LLR \(p\)-Value:** & 0.0001994 \\ \hline  & **coef** & **std err** & **z** & **\(p\)? \(|z|\)** & **[0.025** & **0.975**]** \\ \hline Intercept & \(-\)1.2528 & 0.463 & \(-\)2.706 & 0.007 & \(-\)2.160 & \(-\)0.345 \\ \hline no\_diabetes & 1.2128 & 0.504 & 2.405 & 0.016 & 0.224 & 2.201 \\ \hline diabetic\_antidiabetic\_pills & 1.0857 & 0.546 & 1.988 & 0.047 & 0.015 & 2.156 \\ \hline diabetic\_diet & 2.6878 & 0.680 & 3.955 & 0.000 & 1.356 & 4.020 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Logistic regression output for variables related to diabetes. 0.11% for low- or middle-income countries [40; 41]. Hospitalization expenses are the most significant cost component following the expenditures for the medication [42]. The purpose of this study is represented by prevention, rapid diagnosis and intervention on reducing the risk factors, also in the context of certain comorbidities, which aim to be better understood. The ultimate goal is to improve the prognosis of patients who have heart failure and offer better recommendations. It is also desired to identify the main drivers of heart failure, factors used later to estimate the evolution of the disease and survival. The data of the patients admitted to the hospital for acute heart failure were evaluated and analyzed from the first day of admission to the day of discharge. We can prove that smoking, low education and obesity are the most harmful factors, while diabetes controlled by diet or medication does not significantly affect LVEF; indeed, there is a slight tendency to increase the LVEF. This research has just scratched the surface in the context of measuring and deeply understanding of how certain factors affect HF. However, we believe this approach of measuring where we combine classical and modern machine learning technologies, along with more data could lead to significant progress, also combined with others findings from different areas, such as genetics. Numerous factors are known to contribute to the development of heart failure. The potential causes include coronary artery disease, hypertension, cardiomyopathies, valvular and congenital heart disease, arrhythmias, alcohol and drugs, high output failure (anemia, thyrotoxicosis, Paget's disease, etc. ), pericardial disease, and primary right heart failure. The meta-analyses conducted by [PERSON] et al. [43] found an improvement in the survival rates secondary to CHF over the past 70 years.