Bioinformatics is an interdisciplinary field that uses computer and statistical methods to analyze biological data. This article shows how bioinformatics can be used to diagnose cardiovascular disease. Heart disease affects millions worldwide, making it a serious global health concern. Nowadays, cardiovascular disease affects many more individuals than any other major cause on World. Primary diagnosis of heart disease is crucial because it can reduce the risk of fatal complications like heart attacks, strokes, and death. The detection and treatment of cardiac disease could be dramatically improved by applying machine learning, a promising new technology. The current method uses a machine learning technique called FCMIM-SVM (Fast Conditional Mutual Information-Support vector machine) to diagnose cardiac disease. An optimized chi-squared (CS) mechanism is a machine learning algorithm proposed in this paper for diagnosing heart disease. The CS mechanism is mostly utilized for feature selection. The proposed system uses the GNB (Gaussian Naive Bayes) machine learning model to overcome the limitations in the current technique, which requires less time to train the data. The highest accuracy score is attained using GNB's k-fold cross-validation. The proposed CS-GNB mechanism will achieve maximum accuracy when processing the huge dataset in comparison to the FCMIM-SVM method. This research contrasts the relative contributions of several risk factors for cardiovascular disease, such as age, family history, hypertension, cholesterol levels, smoking, diabetes, obesity, and lack of exercise. Heart disease diagnostics has a bright future. Doctors can diagnose cardiac problems earlier and more precisely than ever with emerging technologies like machine learning. Patients will benefit from earlier therapy and better results as a result.