## 1 Introduction  
Type 2 diabetes (T2D) is an enduring metabolic disorder wherein the blood glucose level changes, and it might be due to the body's incompetence to use its generated insulin [1; 2; 3]. T2D is quite endemic that has plagued the health care systems in developing countries [4]. Diabetes patients are quite vulnerable to stroke and high mortalities [5]. However, the continuous monitoring of blood glucose level performs an eminent part in mitigating and preventing complications of diabetes [6; 7; 8]. Hypertension, which is a root cause of high blood pressure, is a quite normal and harmful condition. As per a World Health Organization (WHO) report, hypertension could provide bases for cardiac arrest, heart swelling, and eventually the failure of heart [9]. In America alone, around 75 million people (1 in 3) are suffering with high blood pressure [10], which is one of the highest factors of death for Americans [11]. In 2009 alone, it was a key factor of for 348,000 Americans deaths and costs 47.5 billion dollars each year [12].  
Due to increasing risk of diabetes and hypertension, recent studies have utilized machine learning algorithms as decision-making tools to diagnose diabetes and hypertension at an early stage, so that preventive action can be taken by individuals. The machine learning algorithms have showed high performance on predicting the diabetes [13; 14; 15; 16] as well as hypertension [17; 18; 19] based on current conditions of individuals. Furthermore, the machine learning-based algorithm random forest (RF) has been proven to be successful at predicting diabetes [20; 21] as well as hypertension [22; 23], with the highest model accuracy compared to other classification models. However, the machine learning algorithms encounter challenging problems, such as outlier data and imbalanced datasets, which can reduce accuracy. Several studies have demonstrated that by removing the outliers while using the Density-based Spatial Clustering of Applications with Noise (DBSCAN) method [24; 25; 26; 27; 28] and utilizing an oversampling method, such as Synthetic Minority Over Sampling Technique (SMOTE) to balance imbalanced data [21; 29; 30; 31; 32; 33; 34; 35; 36; 37], the performance of prediction system is improved.  
Nevertheless, there is no study about model integration between DBSCAN based outlier detection and SMOTE for RF classifier accuracy improvement, specifically for diabetes and hypertension. Thus, this study proposes Hybrid Prediction Model (HPM) by utilizing DBSCAN-based outlier detection, SMOTE, and RF to predict diabetes, as well as hypertension t based on input risk-factors from users. Furthermore, past literature revealed that the recent technologies, such as Internet of Things (IoT), big data, cloud computing, novel biosensors and machine learning can perform a significant part in enhancing the diabetes management [38]. Therefore, the present study has shown that the proposed HPM can be applied to IoT-based healthcare monitoring systems (HMS), offering users an effective way to identify the danger that is involved in high diabetes and hypertension shortly.  
The rest of the paper organization is provided in the next. Section 2 explains the related works on prediction models for diabetes and hypertension, outlier detection methods, and SMOTE. Section 3 presents the dataset and feature selection. In Section 4, the proposed HPM is presented, while in Section 5, the results and discussions are explained. In Section 6, concluding remarks are presented and several limitations and remaining challenges are discussed.