Heart failure (HF) is one of the most prevalent health conditions worldwide. The cost of treating the infection is estimated to rise even more little measures are taken to address it. According to Blecker et al. (2016), the prevalence of heart failure has been on the rise despite the few reported cases of admissions and readmissions in hospitals. One major challenge in the control of heart failure is late detection. Pujades-Rodriguez et al. (2018) report that diagnosing this illness at an early stage can save many lives because medical practitioners will be able to implement evidence-based lifestyle and pharmacological treatment interventions which can delay and possibly prevent its progression. This research identifies practical measures that can be implemented to ensure that chronic conditions (such as heart complications) are detected quickly and early in primary health care. The study conducted by Blecker et al. (2016) utilized electronic health records (EHRs) which were applied in Geisinger Health Systems between 2003 and 2010. The Geisinger Health Systems are integrated healthcare that provides health services in 31 counties of central and northeastern Pennsylvania and involves 41 Community Practice Clinics (Blecker et al., 2016). Concerning the knowledge and driven insights, the study using the EHRs detected a total of 4,644 heart failure cases during clinical diagnosis. The process of identifying patients varied considerably, but results were explained based on several factors, including outpatient diagnosis, Framingham-related symptoms, medication orders, and reconciliation, the outcome of laboratory tests, and patient and reconciliation lists. The research examined a range of other complications which might lead to heart failure. These diseases included hypertension, diabetes, and coronary-related problems known as the knowledge-driven features. From the results obtained, it was confirmed that the most significant known risk factor for heart failure was hypertension-related (Pujades-Rodriguez et al., 2018). From all the selected models used, hypertension was seen as the most dangerous feature aerating in the cases. Cardiac complications, such as diabetes, did not record many instances. The study puts much trust in the knowledge-driven features and includes all of them in the model. However, this research can have profound implications. The results of the investigation revealed that certain knowledge-driven features could not be predicted because of several reasons. The overall quality of the data captured by electronic health records is one of the main concerns associated with using these systems. Based on the findings of their study, Blecker et al. (2016) observe that it is essential to quantify the predictive power and eliminate the features that are not predictive to improve their usage. Another shortcoming of the model is the problem of formulation which focuses on the orthogonal components. This practice results in redundancy among the elements themselves and reduces the function ability of the most significant feature. Therefore, despite their many benefits, electronic health records have several limitations which compromise their efficacy in the primary case. The usage of combining knowledge and data-driven insights in identifying risk factor