Logistic Regression with Hyper Parameter Tuning Optimization for Heart Failure Prediction - Herwanto, Teguh; Kodri, Wan Ahmad Gazali; Aziz, Faruq; Hewiz, Alya Shafira; Riana, Dwiza (Journal Medical Informatics Technology, 2023)
Heart failure is a major public health concern that causes a substantial number of deaths worldwide. Risk factor analysis is required to diagnose and treat patients with heart failure. The logistic regression with hyper parameter tuning optimization is presented in this research, with ejection fraction, high blood pressure, age, and  serum creatinine as relevant risk factors. This study indicates that better data preparation utilizing Deep Learning with hyper parameter adjustment be used to determine the best parameter that has a substantial influence as a risk factor for heart failure. The experiments employed data from the Faisalabad Institute of Cardiology and Allied  Hospital in Faisalabad (Punjab, Pakistan), which included 299 samples. The experimental findings reveal that the proposed approach obtains a recall of 63.16% greater than related works.